Events and related responses between an expert engine and an advanced mobile transaction platform

ABSTRACT

A method is disclosed herein in accordance with an embodiment of the present invention. The method may include deriving at a mobile transaction platform a multi-dimensional context from one or more user transactions and determining at least one life occurrence based, at least in part, on the multi-dimensional context. The one or more user transactions may be conducted through the mobile transaction platform. The one or more user transactions may be stored on a third-party source. In an aspect, the at least one life occurrence has yet to occur. In another aspect of the invention, the life occurrence has already occurred. The multi-dimensional context may include at least one of user location information and life occurrence location information. The multi-dimensional context may include at least one of a time of life occurrence and a current time.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority to and is a continuation of U.S. patentapplication Ser. No. 14/317,896 filed Jun. 27, 2014, the entirety ofwhich is incorporated herein by reference. U.S. Ser. No. 14/317,896claims the benefit of U.S. provisional application Ser. No. U.S.61/841,019 filed Jun. 28, 2013, the entirety of which is incorporatedherein by reference.

This application is related to the following co-owned U.S. patentapplications, the entirety of each is incorporated herein by reference:Ser. No. 13/909,262 filed on Jun. 4, 2013; Ser. No. 11/539,024 filed onOct. 5, 2006; and U.S. Ser. No. 10/284,676 filed on Oct. 31, 2002 nowpatented as U.S. Pat. No. 8,527,380.

This application is also related to the following co-owned U.S. patentapplications, the entirety of each is incorporated herein by reference:U.S. Ser. No. 13/622,433 filed on Sep. 19, 2012; U.S. Ser. No.13/622,462 filed on Sep. 19, 2012; and U.S. Ser. No. 13/622,815 filed onSep. 19, 2012.

Each patent, patent application and other document referenced herein ishereby incorporated by reference in its entirety.

BACKGROUND

1. Field

This application relates to methods and systems of electronictransactions and particularly relates to mobile secure electronictransactions.

2. Description of the Related Art

As the use of mobile devices for performing a wide range ofuser-specific transactions, including healthcare, shopping, financial,personal, business, and the like continues to rise, the burden ofmanaging most aspects of such transactions falls on the mobile user,thereby increasing complexity of a mobile experience for most users.However, the plethora of information available through thesetransactions and other sources of user-related data makes it possible tosubstantially ease the mobile experience. Yet no integrated solution hasbeen established that facilitates a truly user-centric experience withthe aim of fully integrating a user's mobile experience with his/herlifestyle.

SUMMARY

A method is disclosed that may include deriving at a mobile transactionplatform a multi-dimensional context from one or more user transactionsand determining at least one life occurrence based, at least in part, onthe multi-dimensional context. The one or more user transactions may beconducted through the mobile transaction platform. The one or more usertransactions are stored on a third-party source. In an aspect, the atleast one life occurrence has yet to occur. In another aspect of theinvention, the life occurrence has already occurred. Themulti-dimensional context comprises at least one of user locationinformation and life occurrence location information. Themulti-dimensional context may include at least one of a time of lifeoccurrence and a current time.

A computer readable storage medium having data stored thereinrepresenting software executable by a computer is disclosed herein. Thesoftware may include instructions to derive at a mobile transactionplatform a multi-dimensional context from one or more user transactions.The software may further include instructions to determine at least onelife occurrence based, at least in part, on the multi-dimensionalcontext. The one or more user transactions may be conducted through themobile transaction platform. The one or more user transactions may bestored on a third-party source. The at least one life occurrence has yetto occur, in an example. The life occurrence has already occurred, inanother aspect. The multi-dimensional context may include at least oneof user location information and life occurrence location information.The multi-dimensional context may include at least one of a time of lifeoccurrence and a current time.

A method is disclosed herein that may include receiving at a mobiletransaction platform a multi-dimensional context derived from one ormore user transactions. The method may further include determining atleast one life occurrence based, at least in part, on themulti-dimensional context and generating at least one trigger-eventresponsive to the at least one life occurrence. The at least onetrigger-event facilitates at least one user directed mobile action. Theone or more user transactions are conducted through the mobiletransaction platform. The one or more user transactions may be stored ona third-party source. The at least one life occurrence has yet to occur,in an aspect of the invention. The life occurrence has already occurred,in another aspect of the invention. The multi-dimensional context mayinclude at least one of user location information and life occurrencelocation information. The multi-dimensional context may include at leastone of a time of life occurrence and a current time.

A computer readable storage medium having data stored thereinrepresenting software executable by a computer is disclosed herein. Thesoftware may include instructions to receive at a mobile transactionplatform a multi-dimensional context derived from one or more usertransactions. The software may further include instructions to determineat least one life occurrence based, at least in part, on themulti-dimensional context and instructions to generate at least onetrigger-event responsive to the at least one life occurrence. The atleast one trigger-event facilitates at least one user directed mobileaction. The one or more user transactions may be conducted through themobile transaction platform. The one or more user transactions may bestored on a third-party source. The at least one life occurrence has yetto occur in an aspect of the invention. The life occurrence has alreadyoccurred in another aspect. The multi-dimensional context may include atleast one of user location information and life occurrence locationinformation. The multi-dimensional context may include at least one of atime of life occurrence and a current time.

A method is disclosed herein that includes determining a typeclassification for a life occurrence of an individual from amongst aplurality of life occurrences based at least in part on amulti-dimensional data set constructed by an expert engine that receivesanalysis of transactions of the individual conducted through a mobiletransaction platform. The method may further include generating aresolution path that resolves a life occurrence aspect that is common tolife occurrences of the determined life occurrence type classification.The at least one of determining and generating utilizes fuzzy logic inan aspect. The resolution path may be adapted to be executed on a mobiledevice. The steps of determining and generating may be performed on themobile device. The step of determining may include associating lifeoccurrences with resolution paths utilizing fuzzy logic. The step ofgenerating may include associating life occurrences with resolutionpaths utilizing fuzzy logic.

A computer readable storage medium having data stored thereinrepresenting software executable by a computer is disclosed herein. Thesoftware may include instructions to determine a type classification fora life occurrence of an individual from amongst a plurality of lifeoccurrences based at least in part on a multi-dimensional data setconstructed by an expert engine that receives analysis of transactionsof the individual conducted through a mobile transaction platform. Thesoftware may further include instructions to generate a resolution paththat resolves a life occurrence aspect that is common to lifeoccurrences of the determined life occurrence type classification. Theat least one of determining and generating may utilize fuzzy logic. Theresolution path may be adapted to be executed on a mobile device. Thesteps of determining and generating may be performed on the mobiledevice. The step of determining may include associating life occurrenceswith resolution paths utilizing fuzzy logic. The step of generating mayinclude associating life occurrences with resolution paths utilizingfuzzy logic.

A method is disclosed herein that may include determining a type of lifeoccurrence of an individual based, at least in part, on amultidimensional data set and generating a resolution path adapted toaddress the life occurrence via a life occurrence node. In an aspect, atleast one of the determining and generating may be performed accordingto a rule administered by a rules engine that relates life occurrencetypes with available resolution paths and that applies rules to data forthe individual in the multidimensional data set. The multidimensionaldata set may be formed via a mobile transaction platform through whichthe life occurrence node addresses the life occurrence. The lifeoccurrence node may include a mobile phone. The determining andgenerating may be performed on the mobile phone. The rule may relate oneof a type of life occurrence to one of a plurality of resolution paths.The method may further include applying the rule to the multidimensionaldata set.

A computer readable storage medium having data stored thereinrepresenting software executable by a computer is disclosed herein. Thesoftware may include instructions to determine a type of life occurrenceof an individual based, at least in part, on a multidimensional data setconstructed in connection with a mobile transaction platform (MTP)through which the individual conducts transactions instructions togenerate a resolution path adapted to address the life occurrence via alife occurrence node. In an aspect, at least one of the determining andgenerating is performed according to a rule administered by a rulesengine that relates life occurrence types with available resolutionpaths and that applies rules to data for the individual in themultidimensional data set. The multidimensional data set is formed via amobile transaction platform through which the life occurrence nodeaddresses the life occurrence. The life occurrence node may include amobile phone. The determining and generating may be performed on themobile phone. The rule may relate one of a type of life occurrence toone of a plurality of resolution paths. The software may further includeinstructions to apply the rule to the multidimensional data set.

A method is disclosed herein that may include determining a type of lifeoccurrence of an individual based, at least in part, on amultidimensional data set and generating a resolution action that, whenactivated by the individual triggers invocation of a resolution pathadapted to address the life occurrence via a life occurrence node. In anaspect, at least one of the determining and generating is performedaccording to a rule administered by a rules engine that relates lifeoccurrence types with available resolution paths and that applies rulesto data for the individual in the multidimensional data set.

A method is disclosed herein that includes determining a type of lifeoccurrence of an individual based, at least in part, on amultidimensional data set constructed in connection with a mobiletransaction platform (MTP) through which the individual conductstransactions and generating a resolution path adapted to address thelife occurrence via a life occurrence node. The determining the type oflife occurrence may be based, at least in part, on the application of aneural network. The at least one input to the neural network may includedata of the multidimensional data set. The at least one feedback to theneural network may include a plurality of known life occurrences. Theneural network may operate to infer a life occurrence from themultidimensional data set. The multidimensional data set may be formedvia a mobile transaction platform. The life occurrence node may includea mobile phone.

A computer readable storage medium having data stored thereinrepresenting software executable by a computer is disclosed herein. Thesoftware may include instructions to determine a type of life occurrenceof an individual based, at least in part, on a multidimensional data setand instructions to generate a resolution path adapted to address thelife occurrence via a life occurrence node. The determining the type oflife occurrence is based, at least in part, on the application of aneural network. The at least one input to the neural network comprisesdata of the multidimensional data set. The at least one feedback to theneural network may include a plurality of known life occurrences. Theneural network may operate to infer a life occurrence from themultidimensional data set. The multidimensional data set may be formedvia a mobile transaction platform. The life occurrence node may includea mobile phone.

A method is disclosed herein that includes determining a type of lifeoccurrence of an individual based, at least in part, on amultidimensional data set constructed in connection with a mobiletransaction platform (MTP) through which the individual conductstransactions. The method may further include generating a resolutionaction that, when activated by the individual triggers invocation of aresolution path adapted to address the life occurrence via a lifeoccurrence node. The determining the type of life occurrence is based,at least in part, on the application of a neural network.

A method is disclosed herein that includes determining a type of lifeoccurrence of an individual based, at least in part, on amultidimensional data set constructed in connection with a mobiletransaction platform (MTP) through which the individual conductstransactions and generating a resolution path adapted to address thelife occurrence via a life occurrence node. The step of generating theresolution path may be based, at least in part, on the application of aneural network and wherein at least one feedback to the neural networkmay include at least one outcome for at least one individual havingundertaken a resolution path for a the determined type of lifeoccurrence. The at least one input to the neural network comprises dataof the multidimensional data set. The at least one feedback to theneural network may include a plurality of known life occurrences. Theneural network may operate to infer a life occurrence from themultidimensional data set. The multidimensional data set may be formedvia a mobile transaction platform. The life occurrence node may includea mobile phone.

A computer readable storage medium having data stored thereinrepresenting software executable by a computer is disclosed herein. Thesoftware may include instructions to determine a type of life occurrenceof an individual based, at least in part, on a multidimensional data setconstructed in connection with a mobile transaction platform (MTP)through which the individual conducts transactions and instructions togenerate a resolution path adapted to address the life occurrence via alife occurrence node. The step of generating the resolution path isbased, at least in part, on the application of a neural network andwherein at least one feedback to the neural network may include at leastone outcome for at least one individual having undertaken a resolutionpath for a the determined type of life occurrence. The at least oneinput to the neural network may include data of the multidimensionaldata set. The at least one feedback to the neural network may include aplurality of known life occurrences. The neural network may operate toinfer a life occurrence from the multidimensional data set. Themultidimensional data set may be formed via a mobile transactionplatform. The life occurrence node may include a mobile phone.

A method is disclosed herein that includes determining a type of lifeoccurrence of an individual based, at least in part, on amultidimensional data set constructed in connection with a mobiletransaction platform (MTP) through which the individual conductstransactions and generating a resolution action that, when activated bythe individual triggers invocation of a resolution path adapted toaddress the life occurrence via a life occurrence node. The step ofgenerating the resolution action is based, at least in part, on theapplication of a neural network and wherein at least one feedback to theneural network may include at least one outcome for at least oneindividual having undertaken a resolution path for a the determined typeof life occurrence.

A method is disclosed herein that includes determining a type of lifeoccurrence of an individual based, at least in part, on amultidimensional data set and generating a resolution path adapted toaddress the life occurrence via a life occurrence node. In an aspect, atleast one of the determining and generating is based, at least in part,on the application of an algorithm and wherein at least one feedback tothe algorithm may include at least one of an appropriateness of a priorgenerated resolution path and a correctness of a previously determinedlife occurrence. The life occurrence node may include a mobile phone.The multidimensional data set may be formed, at least in part, viaoperation of a mobile transaction platform. The mobile transactionplatform may be resident on the life occurrence node. The lifeoccurrence node may include a mobile phone. The at least one feedback tothe algorithm is among fuzzy logic and neural network elementsperforming the algorithm.

A computer readable storage medium having data stored thereinrepresenting software executable by a computer is disclosed herein. Thesoftware may include instructions to determine a type of life occurrenceof an individual based, at least in part, on a multidimensional data setand instructions to generate a resolution path adapted to address thelife occurrence via a life occurrence node. In an aspect, at least oneof the determining and generating is based, at least in part, on theapplication of an algorithm and wherein at least one feedback to thealgorithm comprises at least one of an appropriateness of a priorgenerated resolution path and a correctness of a previously determinedlife occurrence. The life occurrence node may include a mobile phone.The multidimensional data set may be formed, at least in part, viaoperation of a mobile transaction platform. The mobile transactionplatform may be resident on the life occurrence node. The lifeoccurrence node may include a mobile phone. The at least one feedback tothe algorithm is among fuzzy logic and neural network elementsperforming the algorithm.

A method is disclosed herein that includes determining a type of lifeoccurrence of an individual based, at least in part, on amultidimensional data set constructed in connection with a mobiletransaction platform (MTP) through which the individual conductstransactions and generating a resolution action that, when activated bythe individual triggers invocation of a resolution path adapted toaddress the life occurrence via a life occurrence node. In an aspect, atleast one of the determining and generating is based, at least in part,on the application of an algorithm and wherein at least one feedback tothe algorithm comprises at least one of an appropriateness of a priorgenerated resolution path and a correctness of a previously determinedlife occurrence.

A method is disclosed herein that includes determining a type of lifeoccurrence of an individual based, at least in part, on amultidimensional data set and generating a resolution path adapted toaddress the life occurrence via a life occurrence node. Themultidimensional data set is formed, in part, utilizing data generatedfrom a mobile transaction platform via which an individual conductsmobile transactions. The method may further includes utilizing data froma third party analytics source. The mobile transaction platform may beresident at least in part on the life occurrence node. The lifeoccurrence node may include a mobile phone.

A computer readable storage medium having data stored thereinrepresenting software executable by a computer is disclosed herein. Thesoftware may include instructions to determine a type of life occurrenceof an individual based, at least in part, on a multidimensional data setand generate a resolution path adapted to address the life occurrencevia a life occurrence node. The multidimensional data set may be formed,in part, utilizing data generated from a mobile transaction platform viawhich an individual conducts mobile transactions. The method furtherincludes utilizing data from a third party analytics source. The mobiletransaction platform may be resident at least in part on the lifeoccurrence node. The life occurrence node may include a mobile phone.

A method is disclosed herein that may include determining a type of lifeoccurrence of an individual based, at least in part, on amultidimensional data set and generating a resolution action that, whenactivated by the individual triggers invocation of a resolution pathadapted to address the life occurrence via a life occurrence node. Themultidimensional data set may be formed, in part, utilizing datagenerated from a mobile transaction platform via which an individualconducts mobile transactions.

A method is disclosed herein that includes determining a type of lifeoccurrence of an individual based, at least in part, on amultidimensional data set and generating a resolution path adapted toaddress the life occurrence via a life occurrence node. The step ofgenerating the resolution path is based, at least in part, on a contextof an individual that comprises data from a mobile transaction platformvia which the individual conducts mobile transactions, data from a thirdparty source relating to the individual, and location data of theindividual at a point in time. The mobile transaction platform may beresident on the life occurrence node. The life occurrence node mayinclude a mobile phone. The resolution path may be generated utilizing apre-learned preference from a past transaction of the individual, achange in a pattern of the individual, and at least one of a level ofloyalty to a customer loyalty program, an account status, and a creditcard status. The resolution path may be generated utilizing dataindicative of a purchase by the individual. The resolution path mayinclude at least one trigger related to a level of loyalty points.

A computer readable storage medium having data stored thereinrepresenting software executable by a computer is disclosed herein. Thesoftware may include instructions to determine a type of life occurrenceof an individual based, at least in part, on a multidimensional data setand instructions to generate a resolution path adapted to address thelife occurrence via a life occurrence node. The step of generating theresolution path is based, at least in part, on a context of anindividual that comprises data from a mobile transaction platform viawhich the individual conducts mobile transactions, data from a thirdparty source relating to the individual, and location data of theindividual at a point in time. The mobile transaction platform may beresident on the life occurrence node. The life occurrence node mayinclude a mobile phone. The resolution path may be generated utilizing apre-learned preference from a past transaction of the individual, achange in a pattern of the individual, and at least one of a level ofloyalty to a customer loyalty program, an account status, and a creditcard status. The resolution path may be generated utilizing dataindicative of a purchase by the individual. The resolution path mayinclude at least one trigger related to a level of loyalty points.

A method is disclosed herein that includes determining a type of lifeoccurrence of an individual based, at least in part, on amultidimensional data set and generating a resolution action that, whenactivated by the individual triggers invocation of a resolution pathadapted to address the life occurrence via a life occurrence node. Thestep of generating the resolution action is based, at least in part, ona context of an individual that comprises data from a mobile transactionplatform via which the individual conducts mobile transactions, datafrom a third party source relating to the individual, and location dataof the individual at a point in time.

A method is disclosed herein that includes determining a type of lifeoccurrence of an individual based, at least in part, on amultidimensional data set constructed, at least in part, via interactionwith a mobile transaction platform by which an individual conducts atleast one transaction. The method further includes generating aresolution path adapted to address the life occurrence via a lifeoccurrence node. The resolution path may be based, at least in part, ona combination of an outcome predicted for the individual and anassessment of a risk imposed by the resolution path on a third partyservice provider that supports, at least in part, the resolution path.The assessment of risk may include an assessment of a cumulative risk ofthe service provider with respect to the individual. In an aspect, theassessment of risk may include an assessment of a risk of the individualacross a plurality of service providers. The mobile transaction platformmay be resident on the life occurrence node. The life occurrence nodemay include a mobile phone. The at least one user transaction may bestored on a third-party source.

A computer readable storage medium having data stored thereinrepresenting software executable by a computer is disclosed herein. Thesoftware may include instructions to determine a type of life occurrenceof an individual based, at least in part, on a multidimensional data setconstructed, at least in part, via interaction with a mobile transactionplatform by which an individual conducts at least one transaction andgenerate a resolution path adapted to address the life occurrence via alife occurrence node. The resolution path may be based, at least inpart, on a combination of an outcome predicted for the individual and anassessment of a risk imposed by the resolution path on a third partyservice provider that supports, at least in part, the resolution path.The assessment of risk may include an assessment of a cumulative risk ofthe service provider with respect to the individual. The assessment ofrisk may include an assessment of a risk of the individual across aplurality of service providers. The mobile transaction platform may beresident on the life occurrence node. The life occurrence node mayinclude a mobile phone. The at least one user transaction may be storedon a third-party source.

A method is disclosed herein that may include determining a type of lifeoccurrence of an individual based, at least in part, on amultidimensional data set constructed, at least in part, via interactionwith a mobile transaction platform by which an individual conducts atleast one transaction and generating a resolution action that, whenactivated by the individual triggers invocation of a resolution pathadapted to address the life occurrence via a life occurrence node. Theresolution path may be based, at least in part, on a combination of anoutcome predicted for the individual and an assessment of a risk imposedby the resolution path on a third party service provider that supports,at least in part, the resolution path.

A method is disclosed herein that may include analyzing one or moremobile transactions processed by a mobile transaction platform, lifeoccurrence metadata and user related data derived, at least in part,from third party data sources in order to determine a plurality ofresolution actions in response to a life occurrence and presenting theplurality of resolution actions to a user. The method may furtherinclude pre-configuring at least one mobile transaction to facilitate anexecution of the plurality of resolution actions in response to a userselection of the at least one of the plurality of resolution actions.The method may further include pre-configuring at least one mobiletransaction to facilitate an execution of the plurality of resolutionactions; and performing the at least one mobile transaction. The methodmay further include performing the at least one mobile transaction doesnot require user selection of a transaction. The strep of performing theat least one mobile transaction may not require user selection of aresolution action. The resolution action when activated by theindividual triggers invocation of a resolution path adapted to addressthe life occurrence via a life occurrence node.

A computer readable storage medium having data stored thereinrepresenting software executable by a computer is disclosed herein. Thesoftware may include instructions to analyze one or more mobiletransactions processed by a mobile transaction platform, life occurrencemetadata and user related data derived, at least in part, from thirdparty data sources in order to determine a plurality of resolutionactions in response to a life occurrence and instructions to present theplurality of resolution actions to a user. The computer readable storagemedium may further include instructions to pre-configure at least onemobile transaction to facilitate an execution of the plurality ofresolution actions in response to a user selection of the at least oneof the plurality of resolution actions. The computer readable storagemedium may further include instruction to pre-configure at least onemobile transaction to facilitate an execution of the plurality ofresolution actions; and to perform the at least one mobile transaction.The step of performing the at least one mobile transaction may notrequire user selection of a transaction. The step of performing the atleast one mobile transaction may not require user selection of aresolution action. The resolution action when activated by the usertriggers invocation of a resolution path adapted to address the lifeoccurrence via a life occurrence node.

A method is disclosed herein that may include analyzing one or moremobile transactions processed by a mobile transaction platform, lifeoccurrence metadata and user related data derived, at least in part,from third party data sources in order to determine a plurality ofresolution actions in response to a life occurrence and configuring aplurality of mobile transactions to facilitate the execution of theplurality of resolution actions. The method may further includepresenting the plurality of mobile transactions to a user in response toa detection of at least one trigger event associated with the lifeoccurrence. The life occurrence may be an event in the user's life thathas not yet occurred. In another aspect, the life occurrence may be auser-related event that occurred in the past.

A computer readable storage medium having data stored thereinrepresenting software executable by a computer is disclosed herein. Thesoftware may include instructions to analyze one or more mobiletransactions processed by a mobile transaction platform, life occurrencemetadata and user related data derived, at least in part, from thirdparty data sources in order to determine a plurality of resolutionactions in response to a life occurrence and to configure a plurality ofmobile transactions to facilitate the execution of the plurality ofresolution actions. The software may further include instructions topresent the plurality of mobile transactions to a user in response to adetection of at least one trigger event associated with the lifeoccurrence. The life occurrence is an event in the user's life that hasnot yet occurred in an aspect. The life occurrence is a user-relatedevent that occurred in the past in another aspect.

A mobile transaction platform (MTP) is disclosed herein that may includea transactional analytics facility that analyzes at least one usertransaction conducted with the MTP and creates at least one of a userprofile, a dynamic profile of the user, and a multidimensional contextfor use by an expert engine. The MTP further include the expert enginethat determines a life occurrence based on the multidimensional contextand user-related data from third-party sources, and generates aresolution path of resolution actions that are responsive to one or moreaction trigger-events for resolving one or more aspects of the lifeoccurrence. The MTP further includes at least one life occurrencecontainer deployed on a life occurrence node. The life occurrencecontainer may alert a user of the life occurrence node to the resolutionpath, gather a user response to the alert, and generate one or more lifeoccurrence node-based transactions matched to the resolution path. Thelife occurrence container may be in electronic communication with themobile transaction facility to maintain currency of life occurrences,trigger-events, and resolution actions. The transaction facility and theexpert engine exchange resolution trigger-events, static user profiles,and dynamic user profiles. The expert engine may determine a lifeoccurrence using a combination of at least two of fuzzy logic, machinelearning, and neural networks. The expert engine and the transactionfacility may access one or more ecosystem resources when determining andanalyzing through an enterprise service bus or a utility resource accessswitch. The ecosystem resources may include at least one each of thirdparty analytics, a social network interface, a context driver, an offer,a value added service, a trusted service manager (TSM), a certificateauthoritie (CA), and a database. The life occurrence node may be amobile device. The mobile device may be used to select one of the lifeoccurrence node-based transactions. In an aspect, a personalizedinstrument may be configured to securely cause the life occurrencenode-based transaction matched to the resolution path to be executed bya server. The user transactions and user-related data from third-partysources may be stored in a multi-dimensional database. The analysis bythe transactional analytics facility may produce transactional analyticsdata. The expert engine may be configured to consolidate thetransactional analytics data with data from a third party source of userdata and with a current context in determining the life occurrence. Thecontext may include vendor personalization of a widget executing in thecontainer and at least one context item selected from a list of contextitems consisting of: a time, a location, a transaction detail, anurgency, an importance, the status of a credit card or account, mobiledevice use history, payment source, wallet state, type of transaction,product/service, vendor, delivery method, delivery arrangements, taxstatus, transaction participant, user preferences, the presence of anetwork or a particular account, user associations with a non-vendorthird-party, presence of vouchers and promotions, loyalty points,third-party user-related data, social network information, and calendarinformation. The at least one user profile or dynamic profile may alsoused be in determining the life occurrence.

A non-transitory computer readable medium with an executable programstored thereon is disclosed herein. The program instructs amicroprocessor to perform the steps of determining and resolving a lifeoccurrence. The steps may include analyzing at least one usertransaction conducted with a mobile transaction platform (MTP) to createat least one of a user profile, a dynamic profile of the user, and amultidimensional context, determining a life occurrence based on atleast one of the multidimensional context, the user profile, and thedynamic profile, and user-related data from third-party sources,generating a resolution path of one or more action trigger-events forresolving one or more aspects of the life occurrence, alerting a user,using a life occurrence container deployed on a life occurrence node, tothe resolution path, gathering a user response to the alert, andgenerating one or more life occurrence node-based transactions matchedto the resolution path. The step of determining a life occurrence mayinvolve using a combination of at least two of fuzzy logic, machinelearning, and neural networks. The step of determining and analyzing mayinvolve accessing one or more enterprise resources through at least oneof an enterprise service bus and a utility resource access switch. Theecosystem resources may include at least one each of third partyanalytics, a social network, a context driver, an offer, a value addedservice, a trusted service manager (TSM), a certificate authority (CA),and a database. The life occurrence node may be a mobile device. Themobile device may be used to select one of the life occurrencenode-based transactions. The steps may further include providing apersonalized instrument configured to securely cause the life occurrencenode-based transaction matched to the resolution path to be executed bya server. The user transactions and user-related data from third-partysources may be stored in a multi-dimensional database. The context mayinclude vendor personalization of a widget executing in the containerand at least one context item selected from a list of context itemsconsisting of: a time, a location, a transaction detail, an urgency, animportance, the status of a credit card or account, mobile device usehistory, payment source, wallet state, type of transaction,product/service, vendor, delivery method, delivery arrangements, taxstatus, transaction participant, user preferences, the presence of anetwork or a particular account, user associations with a non-vendorthird-party, presence of vouchers and promotions, loyalty points,third-party user-related data, social network information, and calendarinformation.

A mobile transaction platform (MTP) is disclosed herein that may includea transactional analytics facility that analyzes at least one usertransaction conducted with the MTP and creates at least one of a userprofile, a dynamic profile of the user, and a multidimensional contextfor use by an expert engine. The MTP may further include the expertengine that determines a life occurrence based on the multidimensionalcontext and user-related data from third-party sources, and generates aresolution path of one or more resolution actions for resolving one ormore aspects of the life occurrence; and at least one life occurrencecontainer deployed on a life occurrence node, wherein the lifeoccurrence container executes at least one transaction of at least oneresolution action of the resolution path.

A mobile transaction platform (MTP) is disclosed herein that may includea multidimensional data set of transaction details of transactionsconducted by a user through the MTP and a transactional analyticsfacility for analyzing the multidimensional data set to produce acontext for at least one of a life occurrence determination and aresolution of at least one aspect of a life occurrence. The platform mayfurther include an expert engine that uses the context to determine alife occurrence and at least one resolution path for the lifeoccurrence. The step of determining may involve using a combination ofat least two of fuzzy logic, machine learning, and neural networks. Thecontext may include vendor personalization of a widget executing in alife occurrence enabled container of a mobile device of the user and atleast one context item selected from a list of context items consistingof: a time, a location, a transaction detail, an urgency, an importance,the status of a credit card or account, mobile device use history,payment source, wallet state, type of transaction, product/service,vendor, delivery method, delivery arrangements, tax status, transactionparticipant, user preferences, the presence of a network or a particularaccount, user associations with a non-vendor third-party, presence ofvouchers and promotions, loyalty points, third-party user-related data,social network information, and calendar information. The data in themultidimensional data set may at least be one of client specific dataand service provider specific data. The multidimensional data set may bea user database. The transactional analytics facility may analyze thedata in the context of other users to establish a weighting. Thetransactional analytics facility may analyze the data in the context ofsimilar or interested vendors to establish a weighting. The platform mayfurther include an expert engine configured to consolidate the contextwith at least one third party source of user data in determining thelife occurrence.

A non-transitory computer readable medium with an executable programstored thereon is disclosed herein. The program may instruct amicroprocessor to perform the steps of determining a context fordetermining a life occurrence. The steps may include gatheringtransaction details of transactions conducted by a user through a mobiletransaction platform into a multidimensional data set and analyzing,using a transactional analytics facility. The multidimensional data setmay produce a context for at least one of a life occurrencedetermination and a resolution. The step of determining may involveusing a combination of at least two of fuzzy logic, machine learning,and neural networks. The context may include vendor personalization of awidget executing in a life occurrence enabled container of a mobiledevice of the user and at least one context item selected from a list ofcontext items consisting of: a time, a location, a transaction detail,an urgency, an importance, the status of a credit card or account,mobile device use history, payment source, wallet state, type oftransaction, product/service, vendor, delivery method, deliveryarrangements, tax status, transaction participant, user preferences, thepresence of a network or a particular account, user associations with anon-vendor third-party, presence of vouchers and promotions, loyaltypoints, third-party user-related data, social network information, andcalendar information. The data in the multidimensional data set may atleast be one of client specific and service provider specific. Themultidimensional data set may be a user database. The analysis of thedata may be done in the context of other users to establish a weighting.The analysis of the data may be done in the context of similar orinterested vendors to establish a weighting. The computer readablemedium may further store instructions to perform consolidating thecontext with at least one third party source of user data in determiningthe life occurrence.

An instrument-based mobile transaction platform is disclosed herein thatmay include a transaction facility that handles transactions of aplurality of personal mobile devices registered to perform transactionswith the transaction facility and configured with at least one lifeoccurrence capable executable container, analyzes the transactions, andpopulates a multidimensional context with output from the analysis. Theplatform may further include an expert engine that determines lifeoccurrences based on the multidimensional context and third-partysources of user-related data and that generates a resolution path forresolving one or more aspects of the life occurrence, the resolutionpath having a series of resolution actions that are responsive totrigger-events related to the life occurrence and that lead toresolution of the life occurrence. The platform may further include anenterprise service bus for facilitating access by the expert engine andthe transaction facility to one or more ecosystem resources and at leastone life occurrence container deployed on a life occurrence node. Thelife occurrence container may alert a user of the life occurrence nodeto resolution actions available for addressing an aspect of the lifeoccurrence, gathers a user response to the alert, and provides apersonalized instrument configured to securely cause a lifeoccurrence-based, mobile transaction matched to the resolution action tobe executed by a server. The life occurrence container may be inelectronic communication with the transaction facility to maintaincurrency of life occurrences, trigger-events, and resolution actions.The transaction facility and the expert engine may exchange resolutiontrigger-events, static user profiles, and dynamic user profiles. In anaspect, at least one static user profile or at least one dynamic userprofile may also used be in determining the life occurrence. The expertengine may determine life occurrences using a combination of at leasttwo of fuzzy logic, machine learning, and neural networks. The ecosystemresources may include at least one each of third party analytics, asocial network, a context driver, an offer, a value added service, atrusted service manager (TSM), a certificate authority (CA), and adatabase. The life occurrence node may be a mobile device. The usertransactions and user-related data from third-party sources may bestored in a multi-dimensional database. The analysis by thetransactional analytics facility may produce transactional analyticsdata. The expert engine may be configured to consolidate transactionalanalytics data with at least one of a third party source of user dataand a current context in determining the life occurrence. The contextmay include vendor personalization of a widget executing in thecontainer and at least one context item selected from a list of contextitems consisting of a time, a location, a transaction detail, anurgency, an importance, the status of a credit card or account, mobiledevice use history, payment source, wallet state, type of transaction,product/service, vendor, delivery method, delivery arrangements, taxstatus, transaction participant, user preferences, the presence of anetwork or a particular account, user associations with a non-vendorthird-party, presence of vouchers and promotions, loyalty points,third-party user-related data, social network information, and calendarinformation.

A non-transitory computer readable medium with an executable programstored thereon is disclosed herein. The program instructs amicroprocessor to perform steps of determining and resolving a lifeoccurrence. The steps may include analyzing at least one usertransaction conducted with a mobile transaction platform (MTP) to createa static user profile, a dynamic profile of the user, and amultidimensional context comprising data representing aspects of aplurality of user-specific life occurrences, determining a lifeoccurrence based on user-related data from third-party sources and atleast one of the multidimensional context, the static user profile, andthe dynamic profile, generating a resolution path of one or more actiontrigger-events for resolving one or more aspects of the life occurrence,alerting a user, using a life occurrence container deployed on a lifeoccurrence node, to the resolution path, gathering a user response tothe alert; and providing a personalized instrument configured tosecurely cause a life occurrence-based, mobile transaction matched tothe resolution path to be executed cooperatively with a server. The stepof determining involves using a combination of at least two of fuzzylogic, machine learning, and neural networks. The ecosystem resourcesmay include at least one each of third party analytics, a socialnetwork, a context driver, an offer, a value added service, a trustedservice manager (TSM), a certificate authority (CA), and a database. Thelife occurrence node may be a mobile device. The user transactions anduser-related data from third-party sources may be stored in amulti-dimensional database. The context may include vendorpersonalization of a widget executing in the container and at least onecontext item selected from a list of context items consisting of: atime, a location, a transaction detail, an urgency, an importance, thestatus of a credit card or account, mobile device use history, paymentsource, wallet state, type of transaction, product/service, vendor,delivery method, delivery arrangements, tax status, transactionparticipant, user preferences, the presence of a network or a particularaccount, user associations with a non-vendor third-party, presence ofvouchers and promotions, loyalty points, third-party user-related data,social network information, and calendar information.

An instrument-based life occurrence transaction platform is disclosedherein that may include a transaction facility for handling transactionsof a personal mobile device, analyzing the transactions, and extractinga multidimensional context from the analysis, the multidimensionalcontext comprising data representing aspects of a plurality ofuser-specific life occurrences. The platform may further include anexpert engine that determines user-specific life occurrences based onthe multidimensional context and third-party sources of user-relateddata, and generates a resolution path for resolving one or more aspectsof the occurrence, the resolution path having a series of resolutionactions that are performed based on occurrences of trigger-eventsleading to resolution of the life occurrence. The platform may furtherinclude an enterprise service bus for facilitating access by the expertengine and the transaction facility to one or more ecosystem resourcesand at least one life occurrence container deployed on a life occurrencenode that administers selection of at least one resolution action foraddressing an aspect of the life occurrence, wherein the at least oneresolution action comprises providing a personalized instrumentconfigured to securely cause a life occurrence-based, mobile transactionmatched to the resolution action to be executed cooperatively with aserver. The life occurrence container may be in electronic communicationwith the transaction facility to maintain currency of life occurrences,trigger-events, and resolution actions. The transaction facility and theexpert engine may exchange resolution trigger-events, static userprofiles, and dynamic user profiles. The at least one static profile orat least one dynamic profile may also be used in determining the lifeoccurrence. The expert engine may use a combination of at least two offuzzy logic, machine learning, and neural networks. The ecosystemresources may include at least one each of third party analytics, asocial network, a context driver, an offer, a value added service, atrusted service manager (TSM), a certificate authority (CA), and adatabase. The life occurrence node may be a mobile device. The usertransactions and user-related data from third-party sources may bestored in a multi-dimensional database. The analysis by thetransactional analytics facility may produce transactional analyticsdata. The expert engine may be configured to consolidate transactionalanalytics data with at least one of a third party source of user dataand a current context in determining the life occurrence. The contextmay include vendor personalization of a widget executing in thecontainer and at least one context item selected from a list of contextitems consisting of: a time, a location, a transaction detail, anurgency, an importance, the status of a credit card or account, mobiledevice use history, payment source, wallet state, type of transaction,product/service, vendor, delivery method, delivery arrangements, taxstatus, transaction participant, user preferences, the presence of anetwork or a particular account, user associations with a non-vendorthird-party, presence of vouchers and promotions, loyalty points,third-party user-related data, social network information, and calendarinformation. The instrument may include metadata that identifies atransaction type accessible by a server and user/wallet/deviceinformation required to execute the transaction on behalf of the user.The instrument may be a coupon.

A non-transitory computer readable medium with an executable programstored thereon is disclosed herein. The program may instruct amicroprocessor to perform steps of determining and resolving a lifeoccurrence. The steps may include analyzing at least one usertransaction conducted with a mobile transaction platform (MTP) to createat least one each of a user profile, a dynamic profile of the user, anda multidimensional context comprising data representing aspects of aplurality of user-specific life occurrences, determining a lifeoccurrence based on user-related data from third-party sources and atleast one of the multidimensional context, the user profile, and thedynamic profile, generating a resolution path for resolving one or moreaspects of the life occurrence and providing a personalized instrumentconfigured to securely cause a life occurrence-based, mobile transactionmatched to the resolution action to be executed cooperatively with aserver. The step of determining involves using a combination of at leasttwo of fuzzy logic, machine learning, and neural networks. The ecosystemresources may include at least one each of third party analytics, asocial network, a context driver, an offer, a value added service, atrusted service manager (TSM), a certificate authority (CA), and adatabase. The life occurrence node may be a mobile device. The usertransactions and user-related data from third-party sources may bestored in a multi-dimensional database. The context may include vendorpersonalization of a widget executing in the container and at least onecontext item selected from a list of context items consisting of: atime, a location, a transaction detail, an urgency, an importance, thestatus of a credit card or account, mobile device use history, paymentsource, wallet state, type of transaction, product/service, vendor,delivery method, delivery arrangements, tax status, transactionparticipant, user preferences, the presence of a network or a particularaccount, user associations with a non-vendor third-party, presence ofvouchers and promotions, loyalty points, third-party user-related data,social network information, and calendar information. The instrument mayinclude metadata that identifies a transaction type accessible by aserver and user/wallet/device information required to execute thetransaction on behalf of the user. The instrument may be a coupon.

An instrument-based life occurrence transaction platform is disclosedherein that may include a transaction facility for handling transactionsof a personal mobile device, analyzing the transactions, and providingthe analysis to an expert engine as multidimensional context comprisingdata representing aspects of a plurality of user-specific lifeoccurrences. The platform may further include the expert engine thatdetermines life occurrences based on the multidimensional context andthird-party sources of user-related data, and generates a resolutionpath for resolving one or more aspects of the occurrence, the resolutionpath having a plurality of resolution actions that are optionallyperformed based on occurrences of trigger-events leading to resolutionof the life occurrence. The platform may further include a utilityaccess switch for facilitating access by the expert engine and thetransaction facility to one or more ecosystem resources and at least onelife occurrence container deployed on a life occurrence node thatadministers selection of at least one resolution action for addressingan aspect of the life occurrence. The at least one resolution actioncomprises providing a personalized instrument configured to securelycause a life occurrence-based, mobile transaction matched to theresolution action to be executed cooperatively with a server. The lifeoccurrence container may be in electronic communication with thetransaction facility to maintain currency of life occurrences,trigger-events, and resolution actions. The transaction facility and theexpert engine may exchange resolution trigger-events, static userprofiles, and dynamic user profiles. The at least one static profile orat least one dynamic profile may also be used in determining the lifeoccurrence. The expert engine may use a combination of at least two offuzzy logic, machine learning, and neural networks. The ecosystemresources may include at least one each of third party analytics, asocial network, a context driver, an offer, a value added service, atrusted service manager (TSM), a certificate authority (CA), and adatabase. The life occurrence node may be a mobile device. The usertransactions and user-related data from third-party sources may bestored in a multi-dimensional database. The analysis by thetransactional analytics facility may produce transactional analyticsdata. The expert engine may be configured to consolidate transactionalanalytics data with at least one of a third party source of user dataand a current context in determining the life occurrence. The contextmay include vendor personalization of a widget executing in thecontainer and at least one context item selected from a list of contextitems consisting of: a time, a location, a transaction detail, anurgency, an importance, the status of a credit card or account, mobiledevice use history, payment source, wallet state, type of transaction,product/service, vendor, delivery method, delivery arrangements, taxstatus, transaction participant, user preferences, the presence of anetwork or a particular account, user associations with a non-vendorthird-party, presence of vouchers and promotions, loyalty points,third-party user-related data, social network information, and calendarinformation. The instrument may include metadata that identifies atransaction type accessible by a server and user/wallet/deviceinformation required to execute the transaction on behalf of the user.The instrument may be a coupon.

A non-transitory computer readable medium with an executable programstored thereon is disclosed herein. The program may instruct amicroprocessor to perform steps of determining and resolving a lifeoccurrence that may include analyzing at least one user transactionconducted with a mobile transaction platform (MTP) to create at leastone each of a user profile, a dynamic profile of the user, and amultidimensional context comprising data representing aspects of aplurality of user-specific life occurrences. The steps may furtherinclude determining a life occurrence based on at least one of themultidimensional context, the user profile, and the dynamic profile, anduser-related data from third-party sources, generating a resolution pathfor resolving one or more aspects of the life occurrence and providing apersonalized instrument configured to securely cause a lifeoccurrence-based, mobile transaction matched to the resolution action tobe executed cooperatively with a server. The step of determining mayinvolve using a combination of at least two of fuzzy logic, machinelearning, and neural networks. The ecosystem resources may include atleast one each of third party analytics, a social network, a contextdriver, an offer, a value added service, a trusted service manager(TSM), a certificate authority (CA), and a database. The life occurrencenode may be a mobile device. The user transactions and user-related datafrom third-party sources may be stored in a multi-dimensional database.The context may include vendor personalization of a widget executing inthe container and at least one context item selected from a list ofcontext items consisting of: a time, a location, a transaction detail,an urgency, an importance, the status of a credit card or account,mobile device use history, payment source, wallet state, type oftransaction, product/service, vendor, delivery method, deliveryarrangements, tax status, transaction participant, user preferences, thepresence of a network or a particular account, user associations with anon-vendor third-party, presence of vouchers and promotions, loyaltypoints, third-party user-related data, social network information, andcalendar information. The instrument may include metadata thatidentifies a transaction type accessible by a server anduser/wallet/device information required to execute the transaction onbehalf of the user. The instrument may be a coupon.

An expert engine is disclosed herein that may include a processor thatuses one or more algorithms to consolidate various transactionalanalytics from a mobile transaction platform (MTP) with data from thirdparty sources to produce a multidimensional data set comprising datarepresenting aspects of a plurality of user-specific life occurrences.The expert engine may further include the processor further programmedwith a high-speed algorithm to determine a type of life occurrence of anindividual among a set of possible life occurrences based at least inpart on the multidimensional data set in real-time or near real-time, aresolution path generation facility that generates a plurality ofresolution paths having a series of action events leading to resolutionof at least one life occurrence of the determined type of lifeoccurrence and a communications interface between the MTP and the expertengine that facilitates the sharing of responses to the action eventsbetween the MTP and expert engine, wherein at least one of determiningthe type of life occurrence and generating the plurality of resolutionpaths is based on the shared responses. The high-speed algorithm maydetermine using at least one of temporal data, spatial data and riskassessment. The response to the action events may be via use of a lifeoccurrence node. The life occurrence node may be a mobile device. Theprocessor may further generate a multidimensional context used by thehigh-speed algorithm in determining a life occurrence. The context mayinclude vendor personalization of a widget executing in a lifeoccurrence enabled container of a mobile device of the user and at leastone context item selected from a list of context items consisting of: atime, a location, a transaction detail, an urgency, an importance, thestatus of a credit card or account, mobile device use history, paymentsource, wallet state, type of transaction, product/service, vendor,delivery method, delivery arrangements, tax status, transactionparticipant, user preferences, the presence of a network or a particularaccount, user associations with a non-vendor third-party, presence ofvouchers and promotions, loyalty points, third-party user-related data,social network information, and calendar information.

An expert engine is disclosed herein that may include a processor thatis programmed with a high-speed algorithm to determine a type of lifeoccurrence of an individual among a set of possible life occurrencesbased at least in part on a multidimensional data set comprising datarepresenting aspects of a plurality of user-specific life occurrences.The expert engine may further include a resolution path generationfacility that generates a resolution path having a series of actionevents leading to resolution at least one life occurrence aspect that iscommon to the life occurrences in the determined type of the lifeoccurrence and a communications interface between the MTP and the expertengine that facilitates the sharing of responses to the action eventsbetween the MTP and expert engine, wherein at least one of determiningthe type of life occurrence and generating the resolution path is basedon the shared responses.

A non-transitory computer readable medium with an executable programstored thereon is disclosed herein. The program may instruct amicroprocessor to perform steps of determining a life occurrence andgenerating a resolution path. The steps may include determining, usingan algorithm deployed on a processor, a type of life occurrence of anindividual among a set of possible life occurrences based at least inpart on a multidimensional data set comprising data representing aspectsof a plurality of individual-specific life occurrences, generating,using a resolution path generation facility, a resolution path having aseries of action events leading to resolution of the life occurrence,and sharing responses to the action events from a life occurrence nodewith the processor and resolution path generation facility, wherein atleast one of determining the type of life occurrence and generating theresolution path is based on the shared responses. The algorithm maydetermine using at least one of temporal data, spatial data and riskassessment. The life occurrence node may be a mobile device. The stepsmay further include generating a multidimensional context used by thealgorithm in determining a life occurrence. The context may includevendor personalization of a widget executing in a life occurrenceenabled container of a mobile device of the user and at least onecontext item selected from a list of context items consisting of: atime, a location, a transaction detail, an urgency, an importance, thestatus of a credit card or account, mobile device use history, paymentsource, wallet state, type of transaction, product/service, vendor,delivery method, delivery arrangements, tax status, transactionparticipant, user preferences, the presence of a network or a particularaccount, user associations with a non-vendor third-party, presence ofvouchers and promotions, loyalty points, third-party user-related data,social network information, and calendar information.

A transactional analytics facility is disclosed herein that may includea processor that analyzes user transactions conducted through a mobiletransaction platform (MTP) and third-party sources of user-related datato generate a static user profile and a memory for storing the staticuser profile where it can be accessed by an expert engine in determininga life occurrence based on multidimensional context comprising datarepresenting aspects of a plurality of user-specific life occurrences.The aspects may be derived from analysis of the static user profile, andcurrent context. The current context may include at least one of time,space, and user input. The multidimensional context may include a time,a location, a transaction detail, and at least one of an urgency, animportance, the status of a credit card or account, mobile device usehistory, payment source, wallet state, type of transaction,product/service, vendor, delivery method, delivery arrangements, taxstatus, transaction participant, user preferences, the presence of anetwork or a particular account, user associations with a non-vendorthird-party, presence of vouchers and promotions, loyalty points,third-party user-related data, social network information, and calendarinformation. The current context may include a risk assessment. Thetransactional analytics facility may be in electronic communication witha mobile transaction platform (MTP). The facility may further include auser interface that allows a user to limit which user transactions andthird-party sources of user-related data can be used to generate thestatic user profile.

A transactional analytics facility is disclosed herein that may includea processor that analyzes user transactions conducted through a mobiletransaction platform (MTP) and third-party sources of user-related datato generate a static user profile and a memory for storing the staticuser profile where it can be accessed by an expert engine in determininga life occurrence based on multidimensional context comprising datarepresenting aspects of a plurality of user-specific life occurrences.The aspects may be derived from analysis of the static user profile, andcurrent context. The facility may further include a user interface thatmay allow a user to limit which user transactions and third-partysources of user-related data can be used to generate the static userprofile. The current context may include at least one of time, space,and user input. The multidimensional context may include a time, alocation, a transaction detail, and at least one of an urgency, animportance, the status of a credit card or account, mobile device usehistory, payment source, wallet state, type of transaction,product/service, vendor, delivery method, delivery arrangements, taxstatus, transaction participant, user preferences, the presence of anetwork or a particular account, user associations with a non-vendorthird-party, presence of vouchers and promotions, loyalty points,third-party user-related data, social network information, and calendarinformation. The current context may include a risk assessment. Thetransactional analytics facility may be in electronic communication witha mobile transaction platform (MTP).

A mobile transaction platform (MTP) is disclosed herein that may includea transactional analytics facility that creates a static profile of theuser for use by an expert engine of the MTP and the expert engine thatdetermines a life occurrence based on multidimensional context derivedfrom analysis of user transactions associated with the MTP andthird-party sources of user-related data, and that generates at leastone resolution path for resolving one or more aspects of the lifeoccurrence. The resolution path may include a series of action triggerevents leading to resolution of the life occurrence. The transactionfacility and the expert engine may exchange resolution trigger-events,static user profiles, and dynamic user profiles. The expert engine mayuse a combination of at least two of fuzzy logic, machine learning, andneural networks. The expert engine and the transaction facility mayaccess one or more ecosystem resources when determining and analyzingthrough an enterprise service bus. The ecosystem resources may includeat least one each of third party analytics, a social network, a contextdriver, an offer, a value added service, a trusted service manager(TSM), a certificate authority (CA), and a database. The platform mayfurther include at least one life occurrence container deployed on alife occurrence node. The life occurrence container may alert a user ofthe life occurrence node to the resolution path, gather a user responseto the alert, and generate one or more life occurrence node-basedtransactions matched to the resolution path. The life occurrence nodemay be a mobile device. The mobile device may be used to select one ofthe life occurrence node-based transactions. A personalized instrumentmay be configured to securely cause the life occurrence node-basedtransaction matched to the resolution path to be executed by a server.The user transactions and user-related data from third-party sources maybe stored in a multi-dimensional database. The context may includevendor personalization of a widget executing in a life occurrenceenabled container of a mobile device of the user and at least onecontext item selected from a list of context items consisting of: atime, a location, a transaction detail, an urgency, an importance, thestatus of a credit card or account, mobile device use history, paymentsource, wallet state, type of transaction, product/service, vendor,delivery method, delivery arrangements, tax status, transactionparticipant, user preferences, the presence of a network or a particularaccount, user associations with a non-vendor third-party, presence ofvouchers and promotions, loyalty points, third-party user-related data,social network information, and calendar information. The platform mayfurther include at least one life occurrence container deployed on alife occurrence node that administers selection of at least oneresolution action for addressing an aspect of the life occurrence. Theat least one resolution action may include providing a personalizedinstrument configured to securely cause a life occurrence-based, mobiletransaction matched to the resolution action to be executedcooperatively with a server. The life occurrence node may be a mobiledevice.

A mobile transaction platform (MTP) is disclosed herein that may includea transactional analytics facility that creates a dynamic profile of theuser for use by an expert engine of the MTP and the expert engine thatdetermines a life occurrence based on multidimensional context derivedfrom analysis of user transactions associated with the MTP andthird-party sources of user-related data, and that generates at leastone resolution path for resolving one or more aspects of the lifeoccurrence, the resolution path having a series of action trigger eventsleading to resolution of the life occurrence. The transaction facilityand the expert engine may exchange resolution trigger-events, staticuser profiles, and dynamic user profiles. The expert engine may use acombination of at least two of fuzzy logic, machine learning, and neuralnetworks. The expert engine and the transaction facility may access oneor more ecosystem resources when determining and analyzing through anenterprise service bus. The ecosystem resources may include at least oneeach of third party analytics, a social network, a context driver, anoffer, a value added service, a trusted service manager (TSM), acertificate authority (CA), and a database. The platform may furtherinclude at least one life occurrence container deployed on a lifeoccurrence node. The life occurrence container may alert a user of thelife occurrence node to the resolution path, gather a user response tothe alert, and generate one or more life occurrence node-basedtransactions matched to the resolution path. The life occurrence nodemay be a mobile device. The mobile device may be used to select one ofthe life occurrence node-based transactions. A personalized instrumentmay be configured to securely cause the life occurrence node-basedtransaction matched to the resolution path to be executed by a server.The user transactions and user-related data from third-party sources maybe stored in a multi-dimensional database. The context may includevendor personalization of a widget executing in a life occurrenceenabled container of a mobile device of the user and at least onecontext item selected from a list of context items consisting of: atime, a location, a transaction detail, an urgency, an importance, thestatus of a credit card or account, mobile device use history, paymentsource, wallet state, type of transaction, product/service, vendor,delivery method, delivery arrangements, tax status, transactionparticipant, user preferences, the presence of a network or a particularaccount, user associations with a non-vendor third-party, presence ofvouchers and promotions, loyalty points, third-party user-related data,social network information, and calendar information. The platform mayfurther include at least one life occurrence container deployed on alife occurrence node that administers selection of at least oneresolution action for addressing an aspect of the life occurrence. Theat least one resolution action comprises providing a personalizedinstrument configured to securely cause a life occurrence-based, mobiletransaction matched to the resolution action to be executedcooperatively with a server.

A non-transitory computer readable medium with an executable programstored thereon is disclosed herein. The program may instruct amicroprocessor to perform steps that may include creating a dynamicprofile of the user for use by an expert engine of a mobile transactionplatform (MTP), determining a life occurrence based on multidimensionalcontext derived from analysis of user transactions associated with theMTP and third-party sources of user-related data and generating at leastone resolution path for resolving one or more aspects of the lifeoccurrence, the resolution path having a series of action trigger eventsleading to resolution of the life occurrence. The step of determiningmay involve a combination of at least two of fuzzy logic, machinelearning, and neural networks. The steps may further include deployingat least one life occurrence container on a life occurrence node. Thelife occurrence container may alert a user of the life occurrence nodeto the resolution path, gather a user response to the alert, andgenerate one or more life occurrence node-based transactions matched tothe resolution path. The life occurrence node may be a mobile device.The mobile device may be used to select one of the life occurrencenode-based transactions. A personalized instrument may be configured tosecurely cause the life occurrence node-based transaction matched to theresolution path to be executed by a server. The steps may furtherinclude deploying at least one life occurrence container on a lifeoccurrence node that administers selection of at least one resolutionaction for addressing an aspect of the life occurrence. The at least oneresolution action may include providing a personalized instrumentconfigured to securely cause a life occurrence-based, mobile transactionmatched to the resolution action to be executed cooperatively with aserver.

A transactional analytics facility is disclosed herein that may includea communications facility that gathers multidimensional life occurrencecontext from a mobile transaction platform (MTP) and a processor thatanalyzes user transactions conducted through the MTP. Themultidimensional life occurrence context and third-party sources ofuser-related data may generate a risk profile of a user, trigger-events,third-parties, resolution actions, life occurrences, and potentialtransactions. The risk profile may be used for determining if one ormore resolution actions are suitable for presenting to the user. Therisk profile may be used to rank resolution actions. The risk profilemay relate to the risk of a transaction for a vendor.

A mobile transaction platform (MTP) is disclosed herein that may includea lifestyle container deployed on a life occurrence node that gathersmultidimensional life occurrence context, a transactional analyticsfacility that analyzes data extracted from a plurality of usertransactions by the MTP, third-party sources of user-related data, andthe multidimensional life occurrence context to generate a risk profileof a user, trigger-events, third-parties, resolution actions, lifeoccurrences, and potential transactions and an expert engine that usesthe risk profile to perform a risk-based ranking of resolution actions.The risk profile may further be used by the expert engine fordetermining if one or more resolution actions are suitable forpresenting to the user. The risk profile may relate to the risk of atransaction for a vendor. The life occurrence node may be a mobiledevice.

A mobile transaction platform (MTP) is disclosed herein that may includea lifestyle container deployed on a life occurrence node that gathersmultidimensional life occurrence context, a transactional analyticsfacility that analyzes user transactions conducted by the lifeoccurrence node through the MTP, third-party sources of user-relateddata, and the multidimensional life occurrence context to generate arisk profile of a user, trigger-events, third-parties, resolutionactions, life occurrences, and potential transactions and an expertengine that uses the risk profile to determine if one or more resolutionactions are suitable for presenting to the user. The risk profile mayrelate to the risk of a transaction for a vendor. The expert engine mayfurther use the risk profile to perform a ranking of resolution actions.The life occurrence node may be a mobile device.

A non-transitory computer readable medium with an executable programstored thereon is disclosed herein. The program may instruct amicroprocessor to perform steps that may include gatheringmultidimensional life occurrence context from a mobile transactionplatform (MTP) and analyzing user transactions associated with the MTP.The multidimensional life occurrence context and third-party sources ofuser-related data may generate a risk profile of a user, trigger-events,third-parties, resolution actions, life occurrences, and potentialtransactions. The risk profile may be used for determining if one ormore resolution actions are suitable for presenting to the user. Therisk profile may be used to rank resolution actions. The risk profilemay relate to the risk of a transaction for a vendor.

A non-transitory computer readable medium with an executable programstored thereon is disclosed herein. The program may instruct amicroprocessor to perform steps that may include gatheringmultidimensional life occurrence context, analyzing user transactionsassociated with the MTP, third-party sources of user-related data, andthe multidimensional life occurrence context to generate a risk profileof a user, trigger-events, third-parties, resolution actions, lifeoccurrences, and potential transactions, and ranking resolution actionsbased on the risk profile. The steps may further include determining ifone or more resolution actions are suitable for presenting to the userbased on the risk profile. The risk profile may relate to the risk of atransaction for a vendor.

A non-transitory computer readable medium with an executable programstored thereon is disclosed herein. The program may instruct amicroprocessor to perform steps that may include gatheringmultidimensional life occurrence context, analyzing user transactionsassociated with the MTP, third-party sources of user-related data, andthe multidimensional life occurrence context to generate a risk profileof a user, trigger-events, third-parties, resolution actions, lifeoccurrences, and potential transactions and determining if one or moreresolution actions are suitable for presenting to the user based on therisk profile. The risk profile may relate to the risk of a transactionfor a vendor. The steps may further include performing a ranking ofresolution actions based on the risk profile.

A method for configuring an eco-system enabled life occurrence containeroperating on a mobile device to address a life occurrence is disclosedherein that may include. The method may include developing and storingon a non-transient computer readable medium a context for trigger-eventsbased, at least in part, on life occurrence time data, user and lifeoccurrence location data, transaction analytics of transactionsconducted through a mobile transaction platform of the eco-system, andthird-party user-related data, monitoring the trigger-event context todetect at least one trigger-event indicative of a life occurrence,deploying on the mobile device at least one personalized widgetavailable in the eco-system that facilitates delivery of a third-partyprovided service for addressing the life occurrence, associating atleast one resolution action presented to a user in response to adetected trigger-event with preconfigured mobile transactions forexecuting the at least one resolution action in response to a useracceptance of the presented action, and pre-configuring mobiletransactions that are executed via the personalized widgets to effectdelivery of the third-party service that satisfies an aspect of the lifeoccurrence. The method may further include updating the trigger-eventcontext through an enabling layer operable on the mobile device. Theenabling layer may provide access to at least one trigger-event contextsource. The at least one trigger-event context source comprises at leastone of a GPS data source, a clock and a calendar. The third-partyuser-related data may include at least one of social data, calendar dataand family associations.

A computer readable storage medium having data stored thereinrepresenting software executable by a computer to configure aneco-system enabled life occurrence container operating on a mobiledevice to address a life occurrence is disclosed herein. The softwaremay include instructions to develop and store on a non-transientcomputer readable medium a context for trigger-events based, at least inpart, life occurrence time data, user and life occurrence location data,transaction analytics of transactions conducted through a mobiletransaction platform of the eco-system, and third-party user-relateddata, instructions to monitor the trigger-event context to detect atleast one trigger-event indicative of a life occurrence, instructions todeploy on the mobile device at least one personalized widget availablein the eco-system that facilitates delivery of a third-party providedservice for addressing the life occurrence, instructions to associate atleast one resolution action presented to a user in response to adetected trigger-event with preconfigured mobile transactions forexecuting the at least one resolution action in response to a useracceptance of the presented action and instructions to pre-configuremobile transactions that are executed via the personalized widgets toeffect delivery of the third-party service that satisfies an aspect ofthe life occurrence. The steps may further include updating thetrigger-event context through an enabling layer operable on the mobiledevice. The enabling layer may provide access to at least onetrigger-event context source. The at least one trigger-event contextsource may include at least one of a GPS data source, a clock and acalendar. The third-party user-related data may include at least one ofsocial data, calendar data and family associations.

A mobile device configured for life occurrence resolution is disclosedherein that may include a life occurrence container operable on a lifeoccurrence node operable to coordinate the operation of at least two ofa detection of at least one trigger-event, a use of at least onepersonalized widget, a presentation of at least one resolution actionand an execution of preconfigured actions to facilitate addressing alife occurrence. The mobile device may further include at least onepersonalized widget for facilitating service delivery associated with apreconfigured transaction with a vendor that is determined from analysisof mobile transactions processed through a mobile transaction platform,life occurrence metadata, and user-related data derived from third partyuser data sources. The mobile device may further include an enablinglayer operable on the mobile device for facilitating interoperation ofthe life occurrence container and life occurrence node resourcescomprising at least one of a user interface, communications and secureelement access and at least one electronic wallet operable on the lifeoccurrence node that the personalized widget is authorized to access forfacilitating service delivery. The life occurrence node may be themobile device. The preconfigured actions may include mobiletransactions. The life occurrence may be predicted based, at least inpart, on user-specific mobile transactions processed through a mobiletransaction platform and user-related data derived from third party userdata sources. The service delivery may be facilitated via a servicelayer of a platform for secure personalized transactions.

A non-transitory computer readable medium with an executable programstored thereon is disclosed herein. The program may instruct amicroprocessor to perform steps that may include configuring aneco-system-enabled life occurrence container that is operable on a lifeoccurrence node to facilitate coordinating detection and monitoring oftrigger-events for addressing a life occurrence. The steps ofconfiguring may include generating context at an expert engine fortrigger-events based on at least one of a life occurrence time, a lifeoccurrence location, transaction analytics of user-specific transactionsconducted through a mobile transaction platform, third-partyuser-related data, and a risk to a vendor of a transaction between auser and the vendor and programming the life occurrence container tomonitor the trigger-event context for detection of trigger-events. Theprogram may instruct a microprocessor to perform the steps that mayfurther include synchronizing the life occurrence container with atleast one of the expert engine and a mobile transaction platform (MTP)through which transactions are conducted on behalf of a user via a lifeoccurrence node to maintain a current context for the trigger-events.The step of synchronizing may include updating the trigger-event contextof the life occurrence container through an enabling layer operable onthe life occurrence node. The enabling layer may provide access totrigger-event context sources. The life occurrence node may be a mobiledevice. The trigger-event context sources may include at least one of aGPS, a clock, a calendar, an alert, an e-mail, a message, a call, and abookmark. The life occurrence container may include at least one widget,electronic wallet, resolution action, context monitor, trigger eventdetector, and an enabling layer. The trigger-event context sources mayinclude a time, a location, a transaction detail, and at least one of anurgency, an importance, the status of a credit card or account, mobiledevice use history, payment source, wallet state, type of transaction,product/service, vendor, delivery method, delivery arrangements, taxstatus, transaction participant, user preferences, the presence of anetwork or a particular account, user associations with a non-vendorthird-party, presence of vouchers and promotions, loyalty points,third-party user-related data, social network information, and calendarinformation.

A sync architecture is disclosed herein that may include aneco-system-enabled life occurrence container, that is operable on a lifeoccurrence node, and is configured to facilitate coordinating monitoringand detection of trigger-events for addressing a life occurrence. Thearchitecture may include an expert engine that generates context fortrigger-events based on at least one of time, a location, transactionanalytics, third-party user-related data, and a risk and acommunications facility for synchronizing the life occurrence containerwith at least one of the expert engine and a mobile transaction platform(MTP) through which transactions are conducted on behalf of a user via alife occurrence node to maintain a current context for thetrigger-events. The step of synchronizing may include updating thetrigger-event context of the life occurrence container through anenabling layer operable on the life occurrence node. The enabling layermay provide access to trigger-event context sources. The life occurrencenode may be a mobile device. The trigger-event context sources mayinclude at least one of a GPS, a clock, a calendar, an alert, an e-mail,a message, a call, and a bookmark. The life occurrence container mayinclude at least one widget, electronic wallet, resolution action,context monitor, trigger event detector, and an enabling layer. Thetrigger-event context sources may include a time, a location, atransaction detail, and at least one of an urgency, an importance, thestatus of a credit card or account, mobile device use history, paymentsource, wallet state, type of transaction, product/service, vendor,delivery method, delivery arrangements, tax status, transactionparticipant, user preferences, the presence of a network or a particularaccount, user associations with a non-vendor third-party, presence ofvouchers and promotions, loyalty points, third-party user-related data,social network information, and calendar information.

A non-transitory computer readable medium with an executable programstored thereon is disclosed herein. The program may instruct amicroprocessor to perform steps that may include configuring aneco-system-enabled life occurrence container that is operable on a lifeoccurrence node to facilitate coordinating monitoring and detection oftrigger-events for addressing a life occurrence. The step of configuringmay include generating context at an expert engine for trigger-eventsbased on at least one of time, a location, transaction analytics,third-party user-related data, and a risk. The program may instruct amicroprocessor to perform steps that may further include communicatingamong the life occurrence container, the expert engine and a mobiletransaction platform (MTP) through which a user conducts transactionsvia the life occurrence node to maintain current context for thetrigger-events. The step of communicating may include updating thetrigger-event context of the life occurrence container through anenabling layer operable on the life occurrence node. The enabling layermay provide access to trigger-event context sources. The life occurrencenode may be a mobile device. The trigger-event context sources mayinclude at least one of a GPS, a clock, a calendar, an alert, an e-mail,a message, a call, and a bookmark. The life occurrence container mayinclude at least one widget, electronic wallet, resolution action,context monitor, trigger event detector, and an enabling layer. Thetrigger-event context sources may include a time, a location, and atleast one of a transaction detail, an urgency, an importance, the statusof a credit card or account, mobile device use history, payment source,wallet state, type of transaction, product/service, vendor, deliverymethod, delivery arrangements, tax status, transaction participant, userpreferences, the presence of a network or a particular account, userassociations with a non-vendor third-party, presence of vouchers andpromotions, loyalty points, third-party user-related data, socialnetwork information, and calendar information.

An enhanced communications architecture is disclosed herein that mayinclude an eco-system-enabled life occurrence container, that isoperable on a life occurrence node, and is configured to facilitatecoordinating monitoring and detection of trigger-events for addressing alife occurrence. The architecture may include an expert engine thatgenerates context for trigger-events based on at least one of time, alocation, transaction analytics, third-party user-related data, and arisk and a communications facility for communicating among the lifeoccurrence container, the expert engine and a mobile transactionplatform (MTP) through which a user conducts transactions via the lifeoccurrence node to maintain current context for the trigger-events. Thestep of communicating may include updating the trigger-event context ofthe life occurrence container through an enabling layer operable on thelife occurrence node. The enabling layer may provide access totrigger-event context sources. The life occurrence node may be a mobiledevice. The trigger-event context sources may include at least one of aGPS, a clock, a calendar, an alert, an e-mail, a message, a call, and abookmark. The life occurrence container may include at least one widget,electronic wallet, resolution action, context monitor, trigger eventdetector, and an enabling layer. The trigger-event context sources mayinclude a time, a location, and at least one of a transaction detail, anurgency, an importance, the status of a credit card or account, mobiledevice use history, payment source, wallet state, type of transaction,product/service, vendor, delivery method, delivery arrangements, taxstatus, transaction participant, user preferences, the presence of anetwork or a particular account, user associations with a non-vendorthird-party, presence of vouchers and promotions, loyalty points,third-party user-related data, social network information, and calendarinformation.

A non-transitory computer readable medium with an executable programstored thereon is disclosed herein. The program may instruct amicroprocessor to perform the steps of a life occurrence alert that mayinclude taking metadata that describes a future potential lifeoccurrence, determining possible resolution actions beneficial to takein advance of the future life occurrence based on multidimensionalcontext derived from analysis of transactions performed on behalf of auser with a life occurrence node via a mobile transaction platform andthird-party sources of user-related data, and determining context oftrigger-event conditions for each resolution action, monitoringtrigger-event context. When trigger-event conditions are met, the stepsmay include presenting resolution actions that include life occurrencecontext that is relevant to a user making a decision about accepting theresolution action. The steps may further include preparing an action foreach resolution action and adapting the action based onaction/transaction context when a resolution action is accepted by theuser. The action may be at least one of a mobile device action and atransaction. The preparing of the action may include configuring awidget to access an ecosystem service provider, an electronic wallet onthe user's mobile device, a secure element of the mobile device, and tooptionally trigger other widgets to execute on the mobile device. Thepreparing of the action may include configuring one or more widgets thatfollow user preferences for form of payment, receipt handling, anddelivery/contact details to facilitate service delivery that effects theaction/transaction without requiring user input. The life occurrencenode may be a mobile device. The trigger-event context sources mayinclude at least one of a GPS, a clock, a calendar, an alert, an e-mail,a message, a call, and a bookmark. The life occurrence container mayinclude at least one widget, electronic wallet, resolution action,context monitor, trigger event detector, and an enabling layer. Thetrigger-event context sources may include a time, a location, and atleast one of a transaction detail, an urgency, an importance, the statusof a credit card or account, mobile device use history, payment source,wallet state, type of transaction, product/service, vendor, deliverymethod, delivery arrangements, tax status, transaction participant, userpreferences, the presence of a network or a particular account, userassociations with a non-vendor third-party, presence of vouchers andpromotions, loyalty points, third-party user-related data, socialnetwork information, and calendar information.

A non-transitory computer readable medium with an executable programstored thereon is disclosed herein. The program may instruct amicroprocessor to perform steps of a life occurrence alert that mayinclude taking metadata that describes a future potential lifeoccurrence, determining possible resolution actions beneficial to takein advance of the future life occurrence based on multidimensionalcontext derived from analysis of transactions performed on behalf of auser with a life occurrence node via a mobile transaction platform andthird-party sources of user-related data, and determining context oftrigger-event conditions for each resolution action, monitoringtrigger-event context. When trigger-event conditions are met, the stepsmay include determining if one or more resolution actions are suitablefor presenting to the user. The steps may further include presentingsuitable resolution actions that include life occurrence context that isrelevant to a user making a decision about accepting the resolutionaction, preparing an action for each resolution action, adapting theaction based on action/transaction context when a resolution action isaccepted by the user. The action may be at least one of a mobile deviceaction and a transaction. The step of preparing the action may includeconfiguring a widget to access an ecosystem service provider, anelectronic wallet on the user's mobile device, a secure element of themobile device, and to optionally trigger other widgets to execute on themobile device. The step of preparing the action may include configuringone or more widgets that follow user preferences for form of payment,receipt handling, and delivery/contact details to facilitate servicedelivery that effects the action/transaction without requiring userinput. The life occurrence node may be a mobile device. Thetrigger-event context sources may include at least one of a GPS, aclock, a calendar, an alert, an e-mail, a message, a call, and abookmark. The life occurrence container may include at least one widget,electronic wallet, resolution action, context monitor, trigger eventdetector, and an enabling layer. The trigger-event context sources mayinclude a time, a location, and at least one of a transaction detail, anurgency, an importance, the status of a credit card or account, mobiledevice use history, payment source, wallet state, type of transaction,product/service, vendor, delivery method, delivery arrangements, taxstatus, transaction participant, user preferences, the presence of anetwork or a particular account, user associations with a non-vendorthird-party, presence of vouchers and promotions, loyalty points,third-party user-related data, social network information, and calendarinformation.

A non-transitory computer readable medium with an executable programstored thereon is disclosed herein. The program may instruct amicroprocessor to perform steps of an instrument-based method of lifeoccurrence alert. The steps may include taking metadata that describes apotential life occurrence, determining possible resolution actionsbeneficial to take in advance of the potential life occurrence based onmultidimensional context derived from analysis of user transactionsperformed with a mobile device via a mobile transaction platform andthird-party sources of user-related data, and determining context oftrigger-event conditions for each resolution action, monitoringtrigger-event context. When trigger-event conditions are met, the stepsmay include presenting resolution actions that include life occurrencecontext that is relevant to a user making a decision about accepting theresolution action. The steps may further include preparing an instrumentto facilitate executing at least one of an action and a transaction foreach resolution action and adapting the instrument based on context whena resolution action is accepted by the user. The instrument may includemetadata that identifies a transaction type accessible by a server anduser/wallet/device information required to execute the transaction onbehalf of the user. The action may be a mobile device action. The stepsof preparing the instrument may include configuring a widget to accessan ecosystem service provider, an electronic wallet on the user's mobiledevice, a secure element of the mobile device, and to optionally triggerother widgets to execute on the mobile device. The step of preparing theinstrument may include configuring one or more widgets that follow userpreferences for form of payment, receipt handling, and delivery/contactdetails to facilitate service delivery that effects theaction/transaction without requiring user input. The life occurrencenode may be a mobile device. The trigger-event context sources mayinclude at least one of a GPS, a clock, a calendar, an alert, an e-mail,a message, a call, and a bookmark. The life occurrence container mayinclude at least one widget, electronic wallet, resolution action,context monitor, trigger event detector, and an enabling layer. Thetrigger-event context sources may include a time, a location, and atleast one of a transaction detail, an urgency, an importance, the statusof a credit card or account, mobile device use history, payment source,wallet state, type of transaction, product/service, vendor, deliverymethod, delivery arrangements, tax status, transaction participant, userpreferences, the presence of a network or a particular account, userassociations with a non-vendor third-party, presence of vouchers andpromotions, loyalty points, third-party user-related data, socialnetwork information, and calendar information.

A non-transitory computer readable medium with an executable programstored thereon is disclosed herein that may include. The program mayinstruct a microprocessor to perform steps of an instrument-based methodof life occurrence alert. The steps may include taking metadata thatdescribes a potential life occurrence, determining possible resolutionactions beneficial to take in advance of the potential life occurrencebased on multidimensional context derived from analysis of usertransactions performed with a mobile device via a mobile transactionplatform and third-party sources of user-related data, and determiningcontext of trigger-event conditions for each resolution action,monitoring trigger-event context. When trigger-event conditions are met,the steps may include determining if one or more resolution actions aresuitable for presenting to the user. The steps may further includepresenting suitable resolution actions that include life occurrencecontext that is relevant to a user making a decision about accepting theresolution action, preparing an instrument to facilitate executing atleast one of an action and a transaction for each resolution action, andadapting the instrument based on context when a resolution action isaccepted by the user. The instrument may include metadata thatidentifies a transaction type accessible by a server anduser/wallet/device information required to execute the transaction onbehalf of the user. The action may be a mobile device action. The stepof preparing the instrument may include configuring a widget to accessan ecosystem service provider, an electronic wallet on the user's mobiledevice, a secure element of the mobile device, and to optionally triggerother widgets to execute on the mobile device. The step of preparing theinstrument may include configuring one or more widgets that follow userpreferences for form of payment, receipt handling, and delivery/contactdetails to facilitate service delivery that effects theaction/transaction without requiring user input. The life occurrencenode may be a mobile device. The trigger-event context sources mayinclude at least one of a GPS, a clock, a calendar, an alert, an e-mail,a message, a call, and a bookmark. The life occurrence container mayinclude at least one widget, electronic wallet, resolution action,context monitor, trigger event detector, and an enabling layer. Thetrigger-event context sources may include a time, a location, atransaction detail, and at least one of an urgency, an importance, thestatus of a credit card or account, mobile device use history, paymentsource, wallet state, type of transaction, product/service, vendor,delivery method, delivery arrangements, tax status, transactionparticipant, user preferences, the presence of a network or a particularaccount, user associations with a non-vendor third-party, presence ofvouchers and promotions, loyalty points, third-party user-related data,social network information, and calendar information.

A method for initiating on-boarding for a user is disclosed herein. Themethod may include inputting user related information from a lifestylecontainer for a specific user ID, registering at least one intelligentappliance with the specific user ID, applying at least one rule on theuser related information to determine profile of the user using at leastone of machine learning, fuzzy logic and neural network, and displayinginformation on a display interface corresponding to the lifestylecontainer. The method may further include accessing at least oneexternal source to derive information related to at least to userbehavior, user profile on a social networking site, transaction historyfor a merchant, travel information, and health related information,wherein the derived information is used for determining the profile ofthe user. The displaying of information on the display interface mayinclude displaying a welcome message on creating the user profile at amobile transaction platform. The method may further include analyzingthe user profile and determining offers, notification and messages basedon the analyses of the user profile. The method may further includedisplaying the offers, notification and messages on the displayinterface corresponding to the lifestyle container.

A system for initiating on-boarding for a user is disclosed herein. Thesystem may include a lifestyle container configured to receive inputinformation from a user having a user ID, an at least one intelligentappliance associated with the user ID, an expert engine of a mobiletransaction platform, wherein the expert engine is configured to processthe user related information and facilitate communication with at leastone external source to retrieve information corresponding to the user,at least one rule for determining profile of the user using at least oneof machine learning, fuzzy logic and neural network, and a displayinterface configured to display information for the user. The expertengine may be configured to communicate with the at least one externalsource using an enterprise service bus. The lifestyle container may beconfigured to receive offers, notification and messages from the mobiletransaction platform. The lifestyle container may be configured toreceive offers, notification and messages from the mobile transactionplatform during synchronization process. The mobile transaction platformmay be configured to utilize push message to transmit the offers,notification and messages to the lifestyle container.

A method for facilitating shopping transaction for a user is disclosedherein. The method may include receiving a first shopping list from alifestyle container, determining at least one trigger—eventcorresponding to the first shopping list, wherein the at least onetrigger-event is associated with at least one life occurrence of theuser, modifying the first shopping list based on the behavior pattern ofthe user, transmitting the modified first shopping list to at least onemerchant via an enterprise service bus, transmitting the shopping listfrom the merchant to the user for facilitating selection of at least oneshopping item, and performing a shopping transaction based on theselection of the at least one shopping item. The method may furtherinclude configuring the expert engine to determine the transactionbehavior of the user from the shopping transaction that is based on theselection of the at least one shopping item. The method may furtherinclude adding at least one offer for the at least one shopping itembased on the transaction behavior of the user. The receiving of a firstshopping list from a lifestyle container may include receiving a manualselection of the at least one shopping item from the user. The receivingof a first shopping list from a lifestyle container may includeautomatically selecting at least one offer saved on the lifestylecontainer associated with the user, in another aspect. The at least onetrigger-event may be at least one of a temporal event and a locationevent associated with the life occurrence of the user.

A system for facilitating shopping transaction for a user is disclosedherein that may include a lifestyle container configured to receive afirst shopping list, and a mobile transaction platform configured todetermine at least one trigger—event corresponding to the first shoppinglist. The at least one trigger-event may be associated with at least onelife occurrence of the user. The system may further include an expertengine configured to modify the first shopping list based on thebehavior pattern of the user, to transmit the modified first shoppinglist to at least one merchant via an enterprise service bus and totransmit the shopping list from the merchant to the user forfacilitating selection of at least one shopping item, and an enabledecosystem configured to performing a shopping transaction based on theselection of the at least one shopping item. The at least onetrigger-event may be at least one of a temporal event and a locationevent associated with the life occurrence of the user. The expert enginemay be configured to determine the user behavior using fuzzy logic,machine learning and neural network. The expert engine may be configuredto determine the user behavior using data corresponding to the shoppingtransaction being performed by the enabled ecosystem.

A method is disclosed herein for facilitating parking arrangements for auser. The method may include determining time and location associatedwith the parking requirements of the user, monitoring user behavior forthe at least one parking preference, and generating a list of parkingrequirements for the user. The list of the parking requirements mayinclude at least one parking requirement in accordance with the at leastone parking preference of the user. The method may further includetransmitting the list of parking requirements to at least one parkingprovider, and communicating the status of the parking requirement to theuser in response to a life occurrence. The step of determining time andlocation associated with the parking requirements of the user mayinclude monitoring at least one event that may be extracted from atleast one of a calendar application and travel booking information ofthe user. The monitoring of user behavior for the at least one parkingpreference may include monitoring the user behavior using at least oneof a machine learning, fuzzy logic and neural network. The method mayfurther include determining an availability of pre-booking facility fromthe at least one parking provider. The method may further booking aparking space in accordance with the at least one parking requirement ofthe user when the at least one parking provider support pre-booking ofthe parking space. The method may further include generating a referencenumber for the at least one parking requirement when the at least oneparking provider does not support the pre-booking of the parking space.The method may further include communicating live parking updates to theuser for the reference number, wherein the live parking updates mayinclude at least one of: parking prices and an availability of theparking space in accordance with the at least one parking requirement ofthe user.

A system is disclosed herein for facilitating parking arrangement for auser. The system may include a lifestyle container configured todetermine time and location associated with the parking requirements ofthe user, a transaction platform configured to store the parkingrequirements of the user, an expert engine configured to monitor userbehavior for the at least one parking preference, to generate a list ofparking requirements for the user. The list of the parking requirementsmay include at least one parking requirement in accordance with the atleast one parking preference of the user and to transmit the list ofparking requirements to at least one parking provider. The system mayfurther include an enabled ecosystem configured to communicate thestatus of the at least one parking requirement to the user in responseto an life occurrence. The transaction platform may be furtherconfigured to facilitate synchronizing parking requirements of the userwith the lifestyle container. The expert engine may be furtherconfigured to monitor the user behavior using at least one of: a machinelearning, fuzzy logic and neural network.

These and other systems, methods, objects, features, and advantages of alife occurrence management platform will be apparent to those skilled inthe art from the following detailed description of the preferredembodiment and the drawings. All documents mentioned herein are herebyincorporated in their entirety by reference.

BRIEF DESCRIPTION OF THE FIGURES

The methods and systems of life occurrence management and the followingdetailed description of certain embodiments thereof may be understood byreference to the following figures:

FIG. 1 depicts a high-level system diagram including a mobiletransaction platform (MTP) expert engine (EE) configured to determinelife occurrences from a plurality of data sources, some of which areaccessible to the mobile transaction platform.

FIG. 2 depicts a high-level system diagram including an MTP expertengine (EE) configured to determine types of life occurrences of anindividual and to generate candidate resolution paths for resolving oneor more aspects of the life occurrence.

FIG. 3 depicts a high-level system diagram including an MTP-EEconfigured to generate context-based resolution paths using temporaldata and spatial data.

FIG. 4 depicts a high-level system diagram including an MTP-EEconfigured to determine resolution actions for presenting to a user inresponse to a determined life occurrence.

FIG. 5 depicts a high-level system diagram including an MTP-EEconfigured to determine resolution actions using user profiles.

FIG. 6 depicts a high-level system diagram including an MTP-EEconfigured to communicate via a utility resource access switch to accessecosystem resources.

FIG. 7 depicts a high-level system diagram including an MTP expertengine configured to generate a resolution path having a series ofaction trigger-events.

FIG. 8 depicts a high level system diagram including a life occurrencecontainer configured to facilitate shopping transactions based on actiontrigger-events.

FIG. 9 depicts a high-level system diagram including a life occurrencecontainer that may facilitate coordinating detection of trigger-eventsfor addressing a life occurrence.

FIG. 10-16 depicts various tables that may include a plurality of datatypes and corresponding attributes of the data types.

FIG. 17A-D illustrates an example embodiment of a method forfacilitating on boarding of a user.

FIG. 18A-D depicts an example embodiment of method for facilitatingshopping transaction for the user in the mobile transaction platform.

FIG. 19A-D depicts an example embodiment of a method for facilitatingparking arrangements for the user in response to a life occurrence.

FIG. 20 depicts a block diagram of an embodiment of a life occurrencedetermination and service system.

FIG. 21 depicts a block diagram of an embodiment of a life occurrencedetermination and service system with risk scoring.

FIG. 22 depicts a functional and flow diagram of communications amongfunctional blocks of a life occurrence determination and service system.

FIG. 23 depicts a flow diagram of MTP to expert engine communications.

FIG. 24 depicts a block diagram of an embodiment of a MTP for lifeoccurrence determination and servicing.

FIG. 25 depicts guiding principles for a user centric life occurrencedetermination and servicing capability.

FIG. 26 depicts three views of a life occurrence determination andhandling user interface.

FIGS. 27 through 40 depict various user interfaces for facilitatinginteractions of a user with the lifestyle system through a lifeoccurrence node, in accordance with various embodiments of the presentinvention.

DETAILED DESCRIPTION

A mobile lifestyle that leverages a mobile transaction ecosystem toprovide a range of life occurrence services that maximize usability ofthe ecosystem while minimizing intrusions on the user may enable amanaged process of performance and risk driven escalation of alerts,actions, and transactions to resolve aspects of life occurrences. Withthe premise that a mobile lifestyle experience should revolve around themobile user, thereby establishing a user-centric experience, anintuitive interface that allows a user to view alerts, tokens,notifications and the like is provided. A mobile lifestyle environmentmay also provide a seamless experience with applications drivingtransactions for a wide range of life occurrences covering finance,retail, health, personal, business, government, and the like. Tofacilitate a seamless low-intrusion experience, such a platform mayhandle interfacing to all payment and transaction channels by applying aproactively intelligent capability based around an expert engine-likeenvironment that accepts and utilizes inputs from users, rules,behavioral analytics, all forms of electronic user-related content (e.g.social media), and the like. The result is a mobile user-centricexperience that works to deliver actionable alerts that relate to what auser wants to do rather than just what the user has to do. Driving suchactionable alerts is multi-dimensional context derived from time,location, user mobile uses history, transaction history, third partysourced data, and the like. These actionable alerts are time, location,and context aware while including sufficient flexibility to adjust to auser's reaction to suggestions, and/or recommendations in real time. Theresult being an intuitive system that focuses on simple, seamless,contextual experience that brings all personalized applications into auser lifestyle interface while facilitating all necessary securetransactions behind the scenes thereby not requiring the user to dealwith these complexities.

Life occurrence management services may be extended well beyond directuser mobile device interaction to include any network-connectedcomputing-capable device that is related to or can facilitate resolutionof user-related life occurrences through a secure electronic transactionecosystem. While determining and resolving some life occurrences mayinvolve presentation of resolution path options to a user on the user'smobile or other computing device, life occurrences may be resolvedwithout any need for user notification or interaction, therebyminimizing intrusion on a user, while addressing the resolution actionsthat the user desires. Such automatic life occurrence resolutionservices may be initiated by any life occurrence node (e.g. intelligentelectronic device) or may simply be initiated by an expert engine thathandles determination of and resolution of life occurrences for users.Resolution of aspects of life occurrences, via a secure electronictransaction ecosystem, may include communicating resolution actionsamong life occurrence devices and the ecosystem.

A life occurrence node may be any of a wide range of types of machines,appliances, toys, equipment, packaging, service facilities, healthcaredevices, and the like that are provided with computing and networking. Afew such examples include laundry appliances, drug administrationdevices, self-powered toys, air conditioning units, on-road and off-roadvehicles, automated package delivery vehicles, industrial equipment,home-making appliances, personal health monitoring wearable devices, andthe like. Each exemplary life occurrence node and any other computingand network-capable device may be configured with life occurrenceservicing functionality to generate and/or contribute to the generationof alerts, resolution actions, life occurrence servicing transactions,and the like.

The methods and systems of life occurrence management described hereinmay be optionally activated for a user through an affirmative consent bythe user. Confidentiality of user data, such as user transactional dataat any level of detail, is of paramount of importance when consideringthe embodiments of life occurrence management described herein. Any usermay choose to register with a platform providing life occurrencemanagement services as described herein. An example of such user-chosenregistration is depicted in FIG. 17 described later herein. Without suchan explicit user choice to participate, user information, includingconfidential information available to a mobile transaction platform willremain secure.

The systems and methods of life occurrence management disclosed hereinmay comprise a mobile transaction platform (MTP) expert engine. Such anengine may be used to determine life occurrences based onmultidimensional context derived from user transactions. Suchtransactions may be handled either through the mobile transactionplatform, or third-party sources of user-related data. Life occurrencesmay be an event that has not yet occurred. Life occurrences may be basedon at least one user-related event that has occurred in the past. TheMTP expert engine may use the derived multidimensional context in orderto generate resolution action triggers to resolve such life occurrencesby facilitating user directed mobile actions. In order to facilitatesuch resolution actions, the expert engine may include preconfiguredmobile transaction capabilities to facilitate execution of theresolution actions in response to a user selection of a certainresolution action. Each life occurrence may also be preconfigured withvarious triggers, which may facilitate a user's ability to review andtake action to the resolution action. Such triggers may also beassociated with certain life occurrences so as to alert users to theevents and allow users to select certain resolution paths.

As generally used herein, a resolution path may comprise one or moreresolution actions that lead to a resolution related to a lifeoccurrence, wherein the resolutions actions may be performedindividually. A resolution action that is part of a resolution path mayinfluence the direction of the path (e.g. cause a branching of theresolution path). Alternatively, a resolution action may comprise one ormore resolution paths that when executed lead to a resolution related toa life occurrence. A resolution action that comprises at least oneresolution path may be used to activate the resolution path. Likewise auser may be provided with more than one resolution action and based onwhich resolution action the user chooses, the resolution path associatedwith the selected resolution action will be taken to resolve an aspectof the life occurrence.

The MTP may facilitate a secure mobile transaction. The data associatedwith the transaction, such as the date, time, location, payment source,wallet state, type of transaction, vendor, product type, among others,may be harvested, categorized, aggregated, and processed with othertransaction data for the user. The data collected may also be sent to adata repository from which the expert engine can determine lifeoccurrences and generate resolution paths.

An expert engine may use mobile transaction data sources for generatingaction triggers. Such an expert engine may be coupled with a mobiletransaction platform (MTP) and may use a multidimensional contextderived from user mobile-based transactions handled through the MTP togenerate action triggers for resolving life occurrences by facilitatinguser directed mobile device actions.

The expert engine may determine a type of life occurrence of anindividual among a set of possible life occurrences. The determinationmay be based at least in part on a multidimensional data set constructedin connection with a MTP through which the individual conductstransactions. The expert engine may then generate a resolution action,that when activated, triggers invocation of a resolution path in orderto address or resolve the life occurrence. Such a resolution path may begenerated to operate via use of a life occurrence node, such as a user'smobile device and the like. The expert engine may use numerous sourcesand methods to determine the type of occurrence and subsequentgeneration of a resolution path. The expert engine occurrence detectionand resolution generation is based on fuzzy logic associating lifeoccurrences with resolution paths.

The expert engine may determine the type of life occurrence andsubsequent generation of a resolution path according to a ruleadministered by a rules engine. The rules engine may relate lifeoccurrence types with available resolution paths and apply the rules tothe data for the individual associated with the multidimensional dataset.

An expert engine, based on neural networking, may facilitate determininglife occurrences. Such an expert engine may determine a type of lifeoccurrence of an individual among a set of possible life occurrencesbased at least in part on a multidimensional data set constructed inconnection with a mobile transaction platform (MTP) through which theindividual conducts transactions. The expert engine may also generate aresolution path for resolving one or more aspects of the occurrence viauses of a life occurrence node, such as a user's mobile device and thelike. Determining the type of life occurrence may be performed based onthe application of a neural network the inputs for which include data ofthe type contained in the multidimensional data set and feedback forwhich includes a set of known life occurrences by which the neuralnetwork may learn to infer a life occurrence from the occurrence of datain the multidimensional data set.

The determination of the type of life occurrence is performed based onthe application of a neural network. The inputs for the neural networkmay include data of the type contained in the multidimensional data set.The feedback for the multidimensional data set may include a set ofknown life occurrences by which the neural network may learn to infer alife occurrence from the occurrence of data in the multidimensional dataset. The feedback for the multidimensional data set may also includeoutcomes for sets of individuals having undertaken different resolutionpaths for different types of life occurrences.

The generation of a resolution path to resolve one or more aspects ofthe life occurrence detected is based on user feedback. Users mayprovide feedback indicating whether the determination of the lifeoccurrence is correct or whether the resolution path offered isappropriate. Such user feedback may then be incorporated into the expertengine's algorithms, rules sets, fuzzy logic set, neural network, orother decision-generating engine. Such feedback may be shared betweenthe MTP and the expert engine.

The expert engine may determine a type of life occurrence of anindividual among a set of possible life occurrences based at least inpart on consolidated analytics that may be based on transaction andnon-transaction data. The consolidated analytics may be derived from amultidimensional data set that may be constructed by using data from themobile transaction platform through which the individual conducts mobiletransactions, data from third party analytics data sources, and/orlocation data for the individual at a particular point in time. Suchdata may also be used to generate resolution paths, which are contextualand may be used in conjunction with other data points such as, but notlimited to, when the determination of the resolution path is made orwhere the user is at the time of determination. Other data points thatmay add to the contextual determination of a resolution path may be datasuch as pre-learned preferences from past transactions, past patterns,change in patterns, levels of loyalty to customer loyalty programs,account status or credit card status, urgency or importance of theoccurrence, among others. For example, the expert engine may observefrom third party analytics that a user has begun to purchase certainproducts, such as diapers, or in certain stores, such as Home Depot.Such changes may be indicative of the user becoming a parent or buying ahouse, respectively.

The expert engine may generate triggers related to a level of loyaltypoints. By analyzing transactions, the MTP can ascertain if a user'slevel of loyalty points is high or low. Such information is more thanjust knowledge of the mere membership of a user in a loyalty program.Once such information is consolidated by the expert engine into amultidimensional context, the expert engine may generate triggers topropose offers which are especially attractive when the user redeemssome of his loyalty points, or where an extra amount of loyalty pointscan be collected.

The status of a credit card or account may comprise a context driver.For example, if the expert engine knows about how ‘strained’ a certaincredit card already is, then, depending on the amount to be paid, itmight propose another card. Also, the user might have a preference topay for expensive goods (or travel-related things) with a specificcredit card, because it offers some additional insurance that may bebeneficial in that situation.

In addition to the time/date element that is part of the occurrencedescriptor, one might add an ‘urgency+importance’ attribute to theoccurrence descriptor, and this ‘urgency+importance’ attribute is likelyto be very personal for each user (and the weight might change overtime), and the expert engine can learn such preferences and makeappropriate proposals. For example, one user may like to pay all billsand taxes absolutely on time, so the closer the due date of this kind oftransaction comes the more a certain element of the screen would come tothe top of the list, increase in size or change in color, or have anagging UI dialogue. A different user may not be so focused on thebills, but more on relationships. For such a user, a friend's birthdaywill be more important as a reminder, because she needs to find theperfect present.

The expert engine may be sensitive to transaction risk for serviceproviders. The expert engine may generate a resolution path based on acombination of the outcome predicted for an individual and an assessmentof the risk that would be imposed by the generated resolution path on athird party service provider that may support the resolution path. Byevaluating the risk of a resolution path while generating the resolutionpath, the expert engine may adjust the generation of the resolution pathto mitigate at least a portion of the risk. In this way, risk may bedynamically evaluated and does not have to be predetermined, although itcan be based on predetermined risk factors. Such an assessment of riskmay be based on the cumulative risk to the service provider with respectto the individual user or an assessment may be based on an assessment ofthe cumulative risk the individual places across multiple serviceproviders. Resolution path risk for third parties may be generatedoutside of the expert engine. The expert engine, or an alternatefacility may assess this externally generated risk as part of generatinga resolution path. In addition to resolution path-based risk, the expertengine when attempting to resolve a life occurrence may also assessresolution action risk to third parties. Resolution actions that pose ahigh degree of risk may be discarded rather than being presented to auser or otherwise enacted in response to a determination of a lifeoccurrence. Risk generally may be mitigated by adjusting aspect of theresolution path (e.g. change vendors), the resolution action (e.g.propose an alternate action), preconfigured mobile transactions forresolving the life occurrence (e.g. adjust the transaction to use a cardwith more favorable vendor protection terms), and the like.

An expert engine may facilitate determination and use of resolutionaction to facilitate resolving aspects of a user's life occurrence. Suchan expert engine may determine a plurality of resolution actions forpresenting to a user in response to a life occurrence. The resolutionactions may be determined by analyzing combinations of mobiletransactions processed through a mobile transaction platform, lifeoccurrence metadata, and user-related data derived from third-party userdata sources. The expert engine may further facilitate preconfiguringmobile transactions to facilitate execution of the resolution actions inresponse to a user selection of the resolution actions. In addition,triggers may be associated with the life occurrence to facilitateenabling the user to review and take action on the resolution actions.

An expert engine may facilitate configuring a plurality of mobiletransactions for facilitating execution of a plurality of resolutionactions that are presented to a user in response to detection of atleast one trigger associated with a life occurrence. The resolutionactions may be determined from analyzing combinations of mobiletransactions processed through the mobile transaction platform, lifeoccurrence metadata, and user-related data derived from third-party userdata sources. The life occurrence is an event that has not yet occurredand is based on at least one user-related event that occurred in thepast.

FIG. 1 depicts a high-level diagram of a potential embodiment of a lifeoccurrence management platform 100 including an expert engine configuredto determine life occurrences using a plurality of data sources,including data sources accessible to a mobile transaction platform (MTP)102. The life occurrence management platform 100 may comprise a MTPexpert engine (EE) input device 104 that may be used to facilitatedetermining life occurrences 108 based on multi-dimensional context 110derived from, among other things, user transactions. The lifeoccurrences 108 may be an event that has not yet occurred or may bebased on at least one user-related event that has occurred in the past.

The MTP expert engine 104 may use the derived multi-dimensional context110 in order to generate resolution action trigger-events 112 to resolvesuch life occurrences 108 by facilitating user-directed mobile actions.In order to facilitate resolving the life occurrence 108 via thegenerated resolution action trigger-events 112, the MTP 102 and/or theexpert engine 104 may prepare preconfigured mobile-device compatibletransactions to facilitate execution of the resolution actions and/orpresent the user-directed mobile actions in response to detectedtrigger-events 112. In an example, a corresponding preconfigured mobiletransaction may be executed in response to a user selection of a certainresolution action. Each life occurrence 108 may also be associated withvarious trigger-events 112, so that a user is able to review and selecta resolution action. Such trigger-events 112 may also be associated withcertain life occurrences 106 so as to alert users to the events andallow users to select certain resolution paths 114.

The multi-dimensional context 110 may comprise location informationassociated with resolving a life occurrence, such as user location,resolution path and/or resolution action location information, and thelike. For example, the current location of the user may be determinedusing any location determination technologies such as global positioningsystem (GPS) and the like. The current location associated with thespecific life occurrence may be a location that is not the user'scurrent location (e.g. another person's home, any of a plurality ofwaypoints, and the like) The multi-dimensional context 110 may compriseat least one of time of life occurrence and current time.

The MTP expert engine 104 may determine a type of life occurrence of anindividual among a set of possible life occurrences based at least inpart on consolidated analytics that may be based on transaction andnon-transaction data. The consolidated analytics may be derived from amulti-dimensional database 118 that may be constructed by using datafrom the mobile transaction platform through which the individualconducts mobile transactions, data from third party analytics datasources, location data for the individual at a particular point in time,and a wide range of other data from a range of sources, such as socialmedia, calendars, user contacts, prior life occurrences, userrelationships, and the like.

The multi-dimensional database 118 may be used to store attributesrelated to clients, client devices, services, service providers,merchants, merchant systems, transactions, payments, tokens, receipts,and other items. The multi-dimensional database 118 may store suchinformation in more than one dimension, so that it can be accessed bydifferent applications or for different purposes. In an embodiment, themulti-dimensional database includes three database dimensions (namely,user mobile transactions, user calendar, and user preferences), itshould be appreciated that the number of dimensions may be one, two,three, or any whole number greater than three.

The units of a first dimension of the multi-dimensional database 118 maycorrespond to an attribute of user mobile transactions, such as names ofservice providers, cash values of transactions, types of transactions,date of transactions, quantities of items in transactions, sources ofitems in a transaction, or any other attribute of a mobile transaction.

The units of a second dimension of the multi-dimensional database 118may correspond to an attribute of the user calendar, wherein thisattribute may without limitation comprise names of meeting attendees,types of the calendar events, geographic location of the calendarevents, and so forth.

The units of a third dimension of the multi-dimensional database 118 maycorrespond to an attribute of user preferences for mobile transactions,wherein this attribute may without limitation comprise maximum amountsfor automatic payment of bills, names of preferred payers of bills, nameof preferred payees for certain items, and so forth. Themulti-dimensional database structure may be associated with the mobiletransaction platform.

A structure of the multi-dimensional database 118 may be designed tosupport various functional aspects of the MTP 102 such as a user-centricinterface, a user-centric engine, security aspects, transmissionaspects, hardware and/or software infrastructure that may be associatedwith the MTP 102, an expert system, a self-learning and self-scalingsystem, a secure web-services protocol, distributed infrastructureservices and other functions of the MTP 102.

Information from multiple sources may be populated in themulti-dimensional database 118 in such a way that the attributes of thedata may be set in multiple dimensions, including relationships amongdata items across different dimensions. This enables querying data indifferent ways for different purposes. For example, themulti-dimensional database 118 supports the user-centric engine wherebyvarious data relating to various services, service providers, domains,devices and systems are stored to allow a user to access services thatuse such data. The multi-dimensional database 118 allows the lifeoccurrence management platform to sift through data more efficiently,employing different dimensions that are optimized for particularretrieval tasks. For example, an element of data may betransaction-related. Another dimension may relate to how data isevaluated. A third element of the data might allow static profiles orentries. A fourth element may allow external entities to enter dataassociated with the data. Data may include data related to financialtransactions such as billings, data related to service providers, datarelated to content items, or a host of other kinds of data. Storing datain the multi-dimensional database 118 may assist with applicationthroughput, as data may be stored in a fashion that allows efficientretrieval of data according to a user's specific needs. For example, alearning algorithm or the MTP expert engine 104 as described herein maylearn which services a user tends to use in which circumstances and theMTP expert engine 104 may push data from the multi-dimensional database118 to, for example, a client device to improve performance of suchservices.

A user user-centric engine may look at data of the multi-dimensionaldatabase 118 to attain advantage from the one or more dimensions. Forexample, if a user flies into London, the platform may be aware of thatfact, be aware of past transactions (such as meetings the user had withvarious people in the past), and look at different dimensions of data topropose various transactions. Similarly, the engine may propose multipletransactions to the user, enabled by the data in the multi-dimensionaldatabase 118.

The multi-dimensional database 118 data may also be used to generateresolution paths 114 which are contextual and may be used in conjunctionwith other data points such as, but not limited to, when thedetermination of the resolution path 114 is made or where the user is atthe time of determination. Other data points that may add to thecontextual determination of a resolution path 114 may be data such aspre-learned preferences from past transactions, past patterns, change inpatterns, levels of loyalty to customer loyalty programs, account statusor credit card status, urgency or importance of the occurrence, amongothers. For example, the MTP expert engine 104 may observe from thirdparty analytics that a user has begun to purchase certain products, suchas diapers, or has begun making purchases in certain stores, such asHome Depot. Such changes may be indicative of the user becoming a parentor buying a house, respectively.

The MTP expert engine 104 may generate trigger-events related to a levelof loyalty points. By analyzing transactions, the MTP expert engine 104may ascertain if a user's level of loyalty points is high or low. Suchinformation is more than just knowledge of the mere membership of a userin a loyalty program. Once such information is consolidated by theexpert engine into the multi-dimensional context 110, the MTP expertengine 104 may generate trigger-events 112 to propose offers which areespecially attractive when the user redeems some of his loyalty points,or where an extra amount of loyalty points can be collected.

The status of a credit card or account may comprise a context driver.For example, if the MTP expert engine 104 knows about how ‘strained’ acertain credit card already is, then, depending on the amount to bepaid, it might propose another card. Also, the user might have apreference to pay for expensive goods (or travel-related things) with aspecific credit card, because it offers some additional insurance thatmay be beneficial in that situation.

In addition to time/date elements that may be part of a life occurrencedescriptor, a life occurrence may include an attribute related tourgency or importance that may be very personal for each user (and aweighting of such attributes might change over time). The MTP expertengine 104 can learn such preferences and thereby incorporate them intoappropriate proposals, such as unsolicited offers, resolution actions,and the like. For example, one user may like to pay all bills and taxesabsolutely on time. This preference may place a corresponding level ofimportance on life occurrences related to bill and tax payment-related.Therefore, the closer the current date is to a due date of this kind oftransaction, the more the weighting for resolution actions or resolutionpaths determined to resolve this life occurrence. Such an increase inweighting may impact how a user may be notified of the life occurrenceresolution options prepared by the expert engine 104. In an example of avisual user interface for allowing a user to interact with the lifeoccurrence methods and systems described herein, certain elements ofsuch a user interface display on a user's device screen may come to thetop of a life occurrence resolution action list. Alternatively, elementsin the user interface related to this increasingly urgent and importantlife occurrence might change visually, such as with an increase in sizeor change in color, or have a nagging UI dialogue. A different user maynot be so focused on the bill payment life occurrences, but more onrelationships and the life occurrences related thereto. For such a user,a friend's birthday may become an even more important reminder as thecurrent date creeps closer to the friend's birthday, perhaps because sheneeds to find the perfect present.

The life occurrence management platform 100 enables a user experiencethrough a life occurrence node 120. Examples of a life occurrence node120 may include a user's mobile device that facilitates presentingnotifications of triggered life occurrences derived from a robustmulti-dimensional context 110 with associated consolidated resolutionactions. A life occurrence node 120 may be any networkable device with abasic processing capability, not just a mobile device. Examples of lifeoccurrence nodes 120 are described elsewhere herein. The life occurrencemanagement platform 100 may facilitate communication between the lifeoccurrence node 120 and the external entities 122 via an enterpriseservice bus (ESB) 124. While the MTP 102 operates to facilitate mobiletransactions between the external entities 122 and the life occurrencenode 120, it facilitates passing data between the external entities 122and the life occurrence node 120 without substantively altering theircontent. However, the MTP 102 does acquire and collect for storage in,for example, a transactional analytics database 128, information andmetadata related to various attributes of the transactions enabled bythe MTP 102. For example, the MTP 102 may store in the transactionalanalytics database 128 information related to transaction times,transaction amounts, service provider identifiers, lifeoccurrence-related trigger, user action(s) to effect the transaction,and the like.

In communication with the MTP 102, the MTP expert engine 104 operates toconsolidate various transactional analytics received from the MTP 102with one or more third party sources 130 to create the multi-dimensionalcontext 110 that is suitable for determining life occurrences,developing and maintaining occurrence action triggers, generatingresolution paths that resolve an aspect of a life occurrence via uses ofthe mobile device, and the like. In operation, the MTP expert engine 104may employ one or more algorithms to consolidate various transactionalanalytics from the MTP 102 with data from the third party sources 130 toproduce the multi-dimensional context 110 from which trigger-events maybe produced. Such algorithms may further order and prioritize thedisplay of life occurrence-related alerts to a user of the mobiledevice.

The MTP expert engine 104 of the life occurrence management methods andsystems described herein may be configured to determine services thatmay be offered to clients and service institutions by using data fromthe plurality of sources including the multi-dimensional contextdatabase 118. The MTP expert engine 104 may be configured to processmulti-dimensional information from the plurality of data sources thatmay include direct input specific instructions from a client andconsolidated input from a plurality of service institutions and vendors.For instance, user or client life occurrence management registrationinformation may be compiled based on surveys and interviews. A completeclient profile may be compiled using information from external agencies,the direct information provided by the clients, and other sources ofdata suitable for a robust client profile. Various transaction analysisand records analysis may be conducted by a transaction service providerand may be offered as a service to the client in a desired form andformat. The transaction service provider may feed transaction analysisand records analysis into the MTP expert engine 104 to form input todetermine at least a portion of overall service offerings suitable forpresenting to the clients by a wide range of service providers, such asretailers, banks, merchants, service consolidators, and other aspects ofa life occurrence management platform. The direct input by the clientsand specific flag information from them may form preferences that may beoutlined by the clients to facilitate rapid use of the preferences in anelectronic transaction-oriented computing environment. The consolidatedinput from the service institutions may include information aboutvarious vendors that may affect overall client profiles, andconsequently the services offered to the clients. An output of the MTPexpert engine 104 may be utilized to determine the services beingoffered to the clients and the service Institutions. Further, the MTPexpert engine 104 may be preferably isolated from other processes toensure confidentiality of user/client information designated asconfidential by the clients, or as determined by the MTP expert engineto be confidential in light of similarities of a data item to data itemsdesignated as confidential by the clients. In this way, a user/clientmay not have to designate each item of data as confidential, yet gainthe benefits of confidentiality of relevant information. In an example,the MTP expert engine 104 may analyze data that the current user or anyother user or groups of users have designated as confidential anddetermine properties of such confidential information. When the MTPexpert engine 104 encounters new information that has not yet beendesignated as confidential, it may determine if the new information hascritical properties that are similar to properties of designatedconfidential information and may thereby treat this new information asconfidential. A user may be alerted, based on user preferences, as tothis designation of confidentiality. Alternatively, a user may not bealerted if the similarity of the new information properties toproperties of confidential information is a high degree of similarity.In such a situation, a user may receive a report of new information thathas been designated as confidential by the MTP expert engine 104.

The MTP expert engine 104 of a life occurrence management platform maybe configured to perform vendor data consolidation. The MTP expertengine 104 may collect and assemble a complete profile on the serviceinstitutions and vendors through various sources. The complete profilemay include profile information, products & services information,marketing & advertising information, information in terms of futurereleases of products and services (e.g., “Future Attractions”). Suchprofile information may further be utilized to determine the servicesoffered to the client, service institutions and vendors. Vendor andservice institution profile information may be accessible to thefunctional elements described herein, such as the MTP and the MTP-expertengine, and may be stored in the multi-dimensional database describedherein.

The MTP expert engine 104 may facilitate in providing users seamlessuser-centric life experiences for the plurality of life occurrences.Some seamless user-centric life experience examples are provided hereinwithout limitations. One such example is a flight-based travel scenario,in which when a user's plane lands, the MTP expert engine 104, havingmaintained context of the user and events related to the user mayalready be aware of the flight status and may arrange for a taxi pickupof the user automatically. A life occurrence management platform may notrequire the user to click a phone, select a coupon, etc. The lifeoccurrence management platform may automatically book the taxi andbother the user only if something may not work the way the user wants itto.

The MTP expert engine 104 may be configured to determine lifeoccurrences associated with life occurrence nodes that correspond to auser, such as intelligent devices that include some form of processingcapability and accordingly, generate resolution paths including one ormore resolution actions in accordance with the life occurrences that areassociated with the intelligent devices. For example, an intelligentwashing machine embodiment of a life occurrence node may facilitateactivating a resolution path that includes a laundry detergent purchasecontext. The washing machine may link up to a user's mobile device sothat the user can be given the option of being a trigger for thepurchase of laundry detergent. Alternatively, the purchase of laundrydetergent may be automated so that the washing machine effectively maystart ordering detergent by itself through an enabled secure ecosystem.Determining whether to automatically order laundry detergent may bebased on what the MTP expert engine 104 may learn from analysis of usercontext across a wide range of user transactions. In this way the lifeoccurrence management platform may adapt its actions based on it'slearning from the varying information.

FIG. 2 is a high-level diagram of a potential embodiment of a lifeoccurrence management platform including the MTP expert engine 104 thatmay be configured to determine a type of life occurrence of anindividual and to generate a resolution path for resolving one or moreaspects of the occurrence. As discussed above in conjunction with FIG.1, the MTP expert engine 104 may be configured to determine lifeoccurrences using one or more data sources of the MTP 102. The MTPexpert engine 104 for example may be configured for determining a typeof life occurrence of an individual among a set of life occurrencesbased at least in part on a multi-dimensional database 118 constructedin connection with the MTP 102 through which the individual conductstransaction. The MTP expert engine 104 may be further configured toaccess a resolution path generator 202 to generate a resolution path forresolving one or more aspects of the occurrence via uses of the lifeoccurrence node 120. In various aspects, the determination of thevarious life occurrences 108 and their resolution paths may occur byusing a set of automated algorithms, artificially intelligent systems,and or contextually-controlled actions that may operate in conjunctionor within the MTP expert engine 104.

For example, as shown in FIG. 2, in an aspect, the MTP expert engine 104may determine a type of the life occurrence 108 of an individual among aset of possible life occurrences and generate the resolution path byusing a fuzzy logic 204 that associates life occurrences with availableresolution paths. The fuzzy logic 204 may perform supervisory functionsto let the MTP expert engine 104 learn from contextual information andallow it to make a decision regarding the life occurrence type and/orthe appropriate resolution path for a particular type of lifeoccurrence. In an example, fuzzy rules may be generated from informationcontained in the multi-dimensional data set so that an output of thefuzzy logic 204 may be indicative of the information received from themulti-dimensional database 118 which may include data related to userhistorical transactions in association with defined spatial, temporal,or other constraints and the like. Fuzzy logic 204 may be enabledthrough fuzzy systems and processors that may be configured to allowprocessing of contextual knowledge and project behavioral patterns, andthe like. As an example, if a student heads toward his school forexamination and a temporal contextual information indicates that theexamination may be about to start and the student may be running late,the fuzzy logic 204 may facilitate the expert engine to interpret thecontext and make a decision regarding indicating a resolution path thatsuggests the student about the nearest taxi stand for hiring a taxi sothat the student reaches the school on time.

The MTP expert engine 104 may determine the type of the life occurrenceof the individual among the set of possible life occurrences 108 using aneural network 208. The neural network 208 may process data contained inthe multi-dimensional database 118 and utilize the feedback from thefuzzy logic 204. The feedback may include a set of known lifeoccurrences by which the neural network 208 may learn to infer a lifeoccurrence from the occurrence of data in the multi-dimensional database118. Further, the resolution path generator 202 may generate theresolution path based on the application of the neural network 208 suchthat the neural network 208 may process the data from themulti-dimensional database 118 in conjunction with the feedback, whichincludes outcomes for sets of individuals having undertaken differentresolution paths for different types of life occurrence.

The MTP expert engine 104 may determine a type of life occurrence of anindividual among a set of possible life occurrences and generate theresolution based on rules administered by a rules engine 210 that may beconfigured to relate life occurrence types with available resolutionpaths. The rules engine 210 may apply one or more rules to the data forthe individual in the multi-dimensional database 118 to determine one ormore resolution paths for the determined type of the life occurrence.

The MTP expert engine 104 may determine the type of life occurrence andgenerate the resolution path based on feedback among fuzzy logic 204 andneural network 208 and from users, sensors, another system, etc. as toat least one of the accuracy of the determination of the life occurrenceand the appropriateness of the resolution path.

FIG. 3 is a high-level system diagram depicting the MTP expert engine104 configured to generate the resolution path 114 based on temporaldata 308 and spatial data 302. The resolution path 112 may be based onan overall context of the individual that includes the point in time atwhich the determination is made, data from a mobile transaction platform(MTP) through which the individual conducts mobile transactions, datafrom a third party source relating to the individual, and location datafor the individual at the point in time. For example, the MTP expertengine 104 may determine present location and time of the individualthat may access the life occurrence node 120. Based on the currentlocation and time, the MTP expert engine 104 may accessmulti-dimensional database 118 or third party sources to determine pastactions of the individual. Accordingly, the MTP expert engine 104 mayutilize the resolution path generator 202 to determine one or moreresolutions paths from the contextual data. With regard to time-basedactions, the MTP expert engine 104 may determine resolution paths thatmay include one or more actions such as display of notifications,alerts, suggestions and the like. Similarly, the MTP expert engine 104may utilize location-based information to determine the resolution path114. For example, the MTP expert engine 104 may extract information fromthe multi-dimensional database 118 or third party sources to determinepast actions (e.g., visiting a restaurant) when the individual waspresent at the current location. Accordingly, the resolution pathgenerator 202 may generate resolution path based on the past actions ofthe user at the current location.

The MTP expert engine 104 may perform risk assessment 304 to determinethe resolution path 114. In such a case, the resolution path 114 may bebased on a combination of the outcome predicted for the individual andan assessment of the risk imposed by the resolution path 114 on a thirdparty service provider associated with the resolution path 114. The riskassessment 304 may include an assessment of the cumulative risk of theservice provider with respect to the individual. Alternatively, the riskassessment 304 may include an assessment of the cumulative risk of theindividual across multiple service providers. The risk assessment 304may determine risk scores for each of the resolution paths 114corresponding to at least one life occurrence 108 of the individual.Based on risk score threshold for an individual, the resolution pathgenerator 202 may generate the one or more resolution paths that mayhave the risk score greater than the threshold score. As illustrated,the risk assessment 304 may be disposed within the MTP expert engine104. However, other embodiments are envisioned, such as the riskassessment may be independent of the expert engine 104, may beaccessible through the enterprise bus, and the like.

FIG. 4 is a high-level system diagram depicting an MTP expert engineconfigured to determine resolution actions for presenting to a user inresponse to a life occurrence. A resolution action determination 402 maybe based on analysis that may be obtained from analyzing mobiletransactions processed through the MTP 102, life occurrence metadata,user-related data derived from third party user data sources, and thelike. The MTP 102 may include a transaction pre-configuration module 404that may facilitate execution of the resolution actions in response to auser selection of the resolution actions. The transactionpre-configuration module 404 may facilitate execution of the resolutionactions and based on the analysis, the transaction pre-configurationmodule 404 may perform at least one transaction without requiring userselection of a transaction or a resolution action.

The transaction pre-configuration module 404 may access third-party(e.g. Internet search based) resources for available offers. Thetransaction pre-configuration module 404 may analyze the availableoffers in combination with the multi-dimensional context 110 to selectone or more offers that may be suitable for a transaction in a lifeoccurrence resolution action. The transaction pre-configuration module404 may configure a plurality of mobile transactions for facilitatingexecution of a plurality of resolution actions that are presented to auser in response to detection of at least one trigger-event associatedwith a life occurrence. The resolution actions are determined fromanalyzing mobile transactions processed through a mobile transactionplatform, life occurrence metadata, and/or user-related data derivedfrom third party user data sources.

The life occurrence node 120 may include a lifestyle container 408 thatmay, inter alias, facilitate alerting a user of the life occurrence node120 to resolution paths available for addressing an aspect of the lifeoccurrence. The lifestyle container 408 may also cause mobiletransactions matched to the resolution path based on a user's responseto a life occurrence alert. The lifestyle container 408 may synchronizewith the mobile transaction facility to maintain currency ofoccurrences, trigger-events, and resolution actions.

The systems and methods disclosed herein may comprise a mobiletransaction platform (MTP). The mobile transaction platform may comprisean expert engine for determining life occurrences based onmultidimensional context. The multidimensional context may be derivedfrom an analysis of user transactions associated with the mobiletransaction platform and third party sources of user related data. Theecosystem of resources available to the MTP may include third partyanalytics, social networks, context drivers at networks and gateways,offers and value added services, host systems, trusted service managers,certificate authorities, and databases, among others. The engine mayalso generate resolution paths for resolving the aspects of theoccurrence, where the resolution path has a series of action triggersleading to the resolution. The engine may be based on a combination offuzzy logic, machine learning, and neural networks.

The platform may also comprise a transactional analytics facility. Thefacility may analyze the transactions conducted within the MTP. The dataderived from this facility may be incorporated into a dynamic profile ofthe user for use by the expert engine. The transaction facility and theexpert engine may exchange resolution triggers, static user profiles,and dynamic user profiles. Static user profiles may be used inconjunction with current context data such as time, space, and userinput for the expert engine in order to determine life occurrences.

The MTP may comprise a lifestyle container. The lifestyle container maybe deployable on a mobile device and may facilitate alerting a user ofthe mobile device to resolution paths available for addressing an aspectof the life occurrence. The lifestyle container may also cause mobiletransactions matched to the resolution path based on a user's responseto a life occurrence alert. The lifestyle container may synchronize withthe mobile transaction facility to maintain currency of occurrences,triggers, and resolution actions.

A mobile transaction platform (MTP) may be enhanced to include amultidimensional data set of transaction details of transactionsconducted by a user through the MTP that are framed in context of lifeoccurrences and linked to third-party user-related data. The MTP may befurther enhanced with an analytics facility for analyzing themultidimensional data set to produce context for life occurrencedetermination and resolution. The multidimensional data set may be auser database.

The MTP may be token-based. Such a token based MTP may comprise anexpert engine for determining life occurrences based on multidimensionalcontext derived from analysis of user transactions associated with amobile transaction platform (MTP) and third-party sources ofuser-related data. Similarly, the expert engine may generate aresolution path for such occurrences. The resolution paths may have aseries of action triggers leading to resolution of the life occurrence.The token-based MTP may comprise a transaction facility for handlingtransactions of a personal mobile device, analyzing the transactions,and providing the analysis to the expert engine. The token-based MTP maycomprise additionally an enterprise service bus for facilitating accessby the expert engine and the transaction facility to ecosystemresources. The token-based MTP may also comprise lifestyle containersthat are deployable on a mobile device for facilitating alerting a userof the mobile device to resolution actions available for addressing anaspect of life occurrence. Based on a user's response to an alert, thetoken-based MTP may provide a personalized token configured to securelycause a mobile transaction matched to the resolution action to beexecuted by a server.

An MTP as described herein facilitates among other things secure mobiletransactions (purchase, top-up, inquiry, etc.). The data associated withsuch a transaction (date, time, location, payment source, wallet state,type of transaction, product/service, vendor, portions of the platformused by the vendor (e.g. personalization, etc.) delivery method andarrangements, tax status, widget used, direct transaction or throughtransaction server, etc.) may be harvested, categorized, aggregated, andprocessed with other transaction data for the user by an analyticsfacility of the MTP. Such valuable data and context may also be fed to adata repository from which the expert engine can determine triggers,transactions, life occurrences, resolution paths, and the like.

Transaction data may be analyzed by the MTP in context of other users,similar or interested vendors, etc. to establish some sort of weighting,importance, etc. This analysis might result in determination of a newtrigger, action, or occurrence. By itself it might be sufficient forsuch determination for the user, or it might be combined with other userdata to determine an action. Example of setting an occurrence andaction: Transaction data might indicate that the user has placed anorder for a new rug that has been purchased by other users who alsobought bedroom furniture (therefore the expert engine might determinethat the rug might be suitable for a bedroom). A new action might begenerated for the user to purchase bedroom furniture. The expert enginecan then generate this action for the user once an occurrence and/ortrigger related to the ordered rug is detected (e.g. setting thedelivery date for the rug). Lead-time for the furniture, rug, etc. mightalso be factored into when the occurrence trigger(s) should bescheduled.

FIG. 5 is a high-level system diagram depicting a MTP expert engine 104configured to determine resolutions action using profile of a user. Thesecure transaction platform may comprise a transactional analyticsfacility 128 that may analyze the transactions conducted within the MTP102. The data derived from the transactional analytics facility 128 maybe incorporated into a static user profile 502 and/or a dynamic profile504 of the user for use by the MTP expert engine 104. The transactiondata may be analyzed by the MTP 102 in context of other users, similaror interested vendors, etc to establish some sort of weighting,importance, etc. This analysis may result in determination of a newtrigger-event, action, or occurrence. The transactional analyticsfacility 128 and the MTP expert engine 104 may exchange resolutiontrigger-events, static user profiles 502, and dynamic user profiles 504.The static user profiles 502 may be used in conjunction with currentcontext data such as time, space, and user input for the MTP expertengine 104 in order to determine life occurrences 108.

The MTP 102 may receive user data from a source, such as an externalentity 122, and such as via the ESB 124. The MTP 102 may then transmitthe user data to a user such as a user operating a mobile deviceexecuting the lifestyle container 408. Further, the MTP 102 may transmitthe user data, static user profile 502, and the dynamic user profile 504to the MTP expert engine 104. The MTP expert engine 104 may determinelife occurrences based on the multi-dimensional context 110, and theprofile related data. The MTP expert engine 104 may further generate theresolution path 114 for resolving one or more aspects of the occurrence.The resolution path 114 may comprise a series of action trigger-eventsleading to resolution of the life occurrence. The MTP expert engine 104may generate the resolution path using any of the fuzzy logic 204,neural network 208, machine learning 508 or any combination thereof.

The MTP expert engine 104 may communicate one or more resolution actionsto the MTP 102, which in turn may transmit the one or more resolutionactions to the life occurrence node 120. For example, the static userprofile 502 and the dynamic profile 504 may indicate that the user hasplaced an order for a new rug that has been purchased by other users whoalso bought bedroom furniture. Accordingly, the MTP expert engine 104may determine that the rug may be suitable for the bedroom and generatea new action for the user to purchase bedroom furniture. The lifeoccurrence node 120 equipped with the lifestyle container 408 mayexecute the one or more resolution actions based on the selection of theuser. In addition, the MTP expert engine 104 may communicate a varietyof types of data and perform a range of functions with the MTP 102. Thevariety of data may include notification, alerts, suggestions, time,location, and the like. The functions may include trigger bus exchange,synchronization, reconciling temporal/spatial windows for contextualconsistency. On receiving the one or more resolution actions, thelifestyle container 408 may facilitate alerting a user of the mobiledevice to resolution paths available for addressing an aspect of lifeoccurrence, and based on user response to an alert, the lifestylecontainer 408 may cause life occurrence node-based (e.g. mobile)transactions matched to the resolution paths.

The MTP 102 may be configured to communicate with a personalizedinstrument 510 (e.g., washing machine) that may be configured tosecurely cause a life occurrence-based/mobile transaction matched to theresolution action to be executed by a server 512. The MTP expert engine104 may determine life occurrences based on the multi-dimensionalcontext 110 derived from analysis of user transactions associated withthe MTP 102 and third-party sources of user-related data. The MTP expertengine 104 may generate a resolution path for resolving one or moreaspects of the occurrence. The resolution path may include a series ofaction trigger-events leading to resolution of the life occurrence. Thelife occurrence management platform may include the transactionalanalytics facility 128 for handling transactions of a personal mobiledevice, analyzing the transactions, and providing the analysis to theMTP expert engine 104. The ESB 124 may facilitate access by the MTPexpert engine 104 and the transactional analytics facility 128 toecosystem resources. The lifestyle container 408 deployable on the lifeoccurrence node 120 may facilitate alerting a user of the lifeoccurrence node (e.g. mobile device) to resolution actions available foraddressing an aspect of life occurrence, and based on user response toan alert, the lifestyle container 408 may provide the personalizedinstrument 510 to securely cause a life occurrence-based/mobiletransaction matched to the resolution action to be executed by theserver 512.

The life occurrence node 120 may facilitate administering selection ofat least one resolution action for addressing an aspect of the lifeoccurrence. The resolution action may comprise providing thepersonalized instrument 510 to securely cause a lifeoccurrence-based/mobile transaction matched to the resolution action tobe executed cooperatively with a server 512.

Referring to FIG. 6, a utility access switch 602 is depicted. Theutility access switch 602 may facilitate access to the ecosystemresources such as third party analytics, social networks, contextdrivers and at least one of networks and gateways, offers and valueadded services, host systems, trusted service managers (TSM),certificate authorities (CA), and databases for the MTP expert engine104 and the transactional analytics facility 128.

An expert engine may facilitate determining a type of life occurrence ofan individual among a set of possible life occurrences based at least inpart on a multidimensional data set constructed in connection with amobile transaction platform (MTP) through which the individual conductstransactions and generating a resolution path having a series of actiontriggers leading to resolution of the life occurrence via uses of themobile device. Determining the type of life occurrence and/or generatingthe resolution path is based on feedback shared between the MTP and theexpert engine that is derived from user responses to the action triggersas to at least one of the accuracy of the determination of the lifeoccurrence and the appropriateness of the resolution path.

A transactional analytics facility may facilitate analyzing usertransactions associated with a mobile transaction platform (MTP) andthird-party sources of user-related data to generate a static userprofile for use by an expert engine for determining life occurrencesbased on multidimensional context derived from analysis of the staticuser profile and current context including time, space, and user input.

A mobile transaction platform may include an expert engine fordetermining life occurrences based on multidimensional context derivedfrom analysis of user transactions associated with a mobile transactionplatform (MTP) and third-party sources of user-related data. The expertengine may generate a resolution path for resolving one or more aspectsof the occurrence. The resolution path may have a series of actiontriggers leading to resolution of the life occurrence. In addition, atransactional analytics facility may be employed for analyzing thetransactions conducted with the MTP and creating a dynamic profile ofthe user for use by the expert engine for at least one of determininglife occurrences and generating a resolution path.

FIG. 7 depicts a high-level system diagram including an MTP expertengine 104 configured to generate a life occurrence aspect resolutionpath having a series of resolutions actions, wherein the resolution pathis responsive to one or more trigger-events associated with a lifeoccurrence. As further described elsewhere herein, an individual mayconduct transactions through the MTP 102, thereby making a wide range oftransaction-related information available to the MTP 102. Asillustrated, various forms of data, some of it transaction-specific, maybe exchanged between the expert engine 104 and the MTP 102 including,but not limited to, trigger-event data, sync data, notifications,alerts, suggestions, temporal data, static profiles, dynamic profiles,risk profiles, generic user profiles, and the like. The MTP 102 may beconnected to a user life occurrence node, such as a user's mobiledevice, to exchange important context related information, such as lifeoccurrences, trigger-events, resolution actions, preconfigured mobiletransactions, and the like.

A life occurrence services platform may participate in risk mitigationthrough risk assessment and management. A risk assessment capability 304may be developed in conjunction with the expert engine 104.Alternatively risk assessment capabilities may be configured as separaterisk assessment services 304 b that may be provided by third partiesthat may interface with the platform through an enterprise service bus,such as ESB 124.

The expert engine may be sensitive to transaction risk for serviceproviders. The expert engine may generate a resolution path based on acombination of the outcome predicted for an individual and an assessmentof the risk imposed by the generated resolution path on a third partyservice provider that may support the resolution path. Such anassessment of risk may be based on the cumulative risk to the serviceprovider with respect to the individual user or an assessment may bebased on an assessment of the cumulative risk the individual placesacross multiple service providers.

The expert engine 104 may utilize risk as a context driver whengenerating triggers and attendant resolution actions. For example, asthe MTP 102 executes one or more transactions in response to a user'sinputs in response to an alert of a trigger, the MTP 102 may dynamicallyidentify one or more attributes of the transactions as amounting to anunacceptable risk. In response, the MTP 102 may alert the user to, forexample, chose a different mode of payment or another vendor. Forexample, a user may be provided multiple payment options for proceedingwith the mobile transaction. The user may further have defined a defaultcredit card for mobile transactions that may be selected in the eventthat no other form of payment is selected and/or if a chosen form ofpayment is not acceptable. In this way, the MTP, in cooperation with thecontainer 106 and/or other MTP resources on the mobile device, mayautomatically switch forms of payment and/or vendors in response todetection of an unacceptable level of risk. A non-limiting example ofrisk management, the user may buy neckties and the expert engine 108 mayidentify matching cufflinks and suggest to the user to purchase thematching cufflinks at an identified vendor with the vendor's issuedcredit card. The user agrees and lifestyle container 408 is updated tofacilitate presenting a consolidated view of the transaction. However,expert engine 104 determines that an aspect of risk of this transactionis unacceptable (e.g. payment terms of the vendor's credit card areonerous) and suggests to the user the choice of using a different creditcard that is accessible in the user's mobile wallet via the mobiledevice instead of the store card, even though the user will lose out onsome vendor-specific loyalty points.

A life occurrence management platform may include or be communicativelycoupled with a central ID management system. A central ID managementcapable life occurrence management platform may be used to implementlife occurrence servicing as described herein. A Profile and IDmanagement and authentication capability may be separated out from corelife occurrence management platform elements into a separate system thatmay communicate over a transaction support bus, such as the ESB 124 tofacilitate handling ID-related functions associated with life occurrenceservicing. Alternatively, a switching and brokering interface may beprovided between the life occurrence handling elements (e.g. MTP, expertengine, and the like) and a separate ID management system rather thanthe ESB. ID related functions, such as security functions (e.g.cryptography, etc.) may also be pulled out in a separate system coupledwith a life occurrence management platform such as for security, trustmodels, approaches for authentication and the like. An ID-awarecontainer may be provided for life occurrence nodes using ID functionsto securely execute mobile applications and other types of applications.

ID may be used as a handle to the profile in a life occurrencemanagement platform and the profile can be of various types. Theseprofiles may comprise static profile that may encompass a person or auser, a dynamic profile, a risk profile, and the like. ID may beassociated with triggers and events. ID life occurrence managementplatform may interact with the risk system.

Feedback responses may be shared between the MTP 102 and the MTP expertengine 104. The feedback responses may be derived from user responses topresented resolution action and may be useful to determine at least oneof a measure of accuracy of a life occurrence determination by theexpert engine and a degree of appropriateness of a particular resolutionpath suggested by the expert engine.

A transactional analytics facility 128 may populate and maintain atransactional analytics data store by analyzing the user transactionsassociated with the MTP 102 and the third-party sources of user-relateddata, thereby generating at least a static user profile. The MTP expertengine 104 may utilize the static user profile for determining lifeoccurrences based on the multi-dimensional context 110. Themulti-dimensional context 110 may be derived from analysis of the staticuser profile 502 and current context including temporal data 308,spatial data 302 and user input.

A transactional analytics facility may also analyze the transactionsconducted with the MTP 102 and create a portion of a dynamic profile ofthe user. A dynamic user profile may be used by the MTP expert engine104 for at least one of determining life occurrences and generating aresolution path.

The transactional analytics facility 128 may analyze the usertransactions associated with the MTP 102 and third-party sources ofuser-related data to generate a risk profile of users. The risk profileof the users of the MTP expert engine 104 may be used to generatetrigger-events, resolution actions, life occurrences, potentialtransactions, and the like based on multi-dimensional life occurrencecontext. The risk profile may be useful for determining if one or moreresolution actions are suitable for presenting to a user. The riskprofile may be used for ranking resolution actions.

In the context of risk profiling, a life occurrence management platformmay utilize user risk profiling to allow merchants to maximize checkoutconversion rates while also decreasing fraud on transactions through useof a risk based authentication system and dynamic multi-factorauthentication methods. Exemplary use cases of risk profile basedtransactions, without limitations, are presented herein below.

In an example, a merchant may be able to choose a type of Checkoutduring Account Management to accomplish a preferred degree of riskmanagement. The life occurrence management platform may re-use existingfunctionality for merchants to opt into advanced checkout and onboard 3DSecurity authentication information. As part of account management,merchants may be able to opt in to different levels of authentication.The life occurrence management platform may provide a way for MerchantService Providers to make choices on behalf of their merchants. The lifeoccurrence management platform may be able to capture necessary datafields as part of bulk merchant upload file, including withoutlimitations a channel of a merchant, Merchant Category Code (MCC/akacard acceptor business code), Merchant ID (MID), merchant accountinformation at a payment gateway in order to process/accept payments,Acquirer ID, and the like. The life occurrence management platform maybe able to update content on existing Advanced Checkout placements toreflect additional functionality.

In an example, a user may want to be able to choose how he would likehis cards to be treated in advanced and verified Checkout while makingtransactions through a life occurrence management platform. The lifeoccurrence management platform may maintain a batch file, which maycontain Card Issuer preferences at the Bank Identification Number (BIN)level.

In an example, a life occurrence management platform may be able to runJavaScript to capture device details during checkout. The lifeoccurrence management platform may be able to capture device ID andredirect checkout experience, including eCom, mobile web, and native appimplementations and the like. The life occurrence management platformmay be able to capture device ID in an API-based one click duringcheckout (full Primary Account Number (PAN) requested), including eCom,mobile web, and native app implementations. The life occurrencemanagement platform may be able to capture device ID outside of checkoutin cases where not all data will be present. This may include APIpairing, Account Management, or future functionality. This may includeeCom, mobile web, and native app implementations and the like.

In an example, the life occurrence management platform may determine thetype of log in method for each session and normalize the selection. Thelife occurrence management platform may be able to create, assign andedit a ‘login method’ for each wallet. The life occurrence managementplatform may be able to execute an API one-click checkout that may beassigned a unique login method and may override the wallet value. EachAPI one-click checkout may be assigned its own value. The lifeoccurrence management platform may create a mapping table that matchesall the discrete login method values to generic values supported by thesystem. The life occurrence management platform may be able to create,assign, or edit for login methods and their mappings outside of aquarterly release, including new values on either end.

In an example, a life occurrence management platform may record how eachcard of a user has been previously authenticated for transaction by thesystem, and update the value on subsequent authentications. The lifeoccurrence management platform may track the strongest authenticationmethod used for every PAN within each wallet account. The lifeoccurrence management platform may update the card authentication valueduring card add and edit or during checkout, and the like. The lifeoccurrence management platform may create ability to add newverification methods (or break into more granular types) or re-orderstrength of each method for example Unverified methods, CardVerification Code (CVC) validation, Address Verification Schemes (AVS)w/CVC validation, Camera/Video capture, SecureKey NFC, 3DS, 3DS—One TimePasscode (OTP), Direct Provisioned where card issuer=wallet issuer, orother methods. The life occurrence management platform may update cardverification status to an equal, or stronger method and may authenticateit successfully. If a card fails explicit authentication, itsverification method may be downgraded to Unverified by a life occurrencemanagement platform. The life occurrence management platform mayidentify if a PAN is on a current ‘fraud’ list and add flag such as OnSystem to Avoid Fraud Effectively (SAFE) list, High chargeback rate, OnIssuer Provided Account Status List, and the like.

In an example, the life occurrence management platform may provide cardverification status during checkout for certain cards as an API wallet.The life occurrence management platform may add card verification statusas a checkout parameter for API wallets. If API wallet provides cardverification status for a PAN during checkout, the life occurrencemanagement platform may for example use provided value only if cardissuer=wallet issuer. The life occurrence management platform may createBIN table to determine if wallet issuer=wallet issuer. For API wallet,if card issuer=/wallet issuer, then the life occurrence managementplatform may ignore the provided value and update the card verificationstatus for the card only after successful step-up authentication. In anexample, the life occurrence management platform may be able tonormalize card verification methods to Risk Based Decisioning (RBD)supported generic values. The life occurrence management platform maycreate a mapping table between each card verification method and thegeneric values supported by a RBD system. The mappings may be able to beadded or changed outside of a quarterly release.

In an example, the life occurrence management platform may assign aconfidence interval to indicate if account owner is likely a fraudsteror a legitimate customer and normalize it as a generic value for theRBD. In an example, the confidence interval may be the strongest cardverification method of a card in a wallet. The confidence interval maybe mapped to the RBD generic value (e.g. strong, medium, weak). In anexample, the life occurrence management platform may create rules thatmay combine the card verification methods for multiple cards in thewallet with the level of contact information verification, and then mapto the confidence interval RBD supported values (e.g. 2 cards withmedium verification methods+email verified+phone verified=highconfidence interval). The life occurrence management platform mayidentify the strongest card verification method used for the cards inthe wallet account. The life occurrence management platform may updatethe wallet account status after every card add, edit, or deleteselections. The life occurrence management platform may update alsoafter email or phone validation. The life occurrence management platformmay identify whether the account's contact details have been validatedfor example it may determine if email address and mobile phone isvalidated. In an example, the life occurrence management platform maydetermine whether to request a trust score from an RBD platform so as tominimize costs. The life occurrence management platform may create logicfor this purpose.

In an example, the life occurrence management platform may collect andaggregate data from all wallet types to provide it to the RBD platformduring either a recommendation request or a data-contribution onlyrequest. The life occurrence management platform may determine whetherto submit a full recommendation request, which requires RBD to return arecommendation and score or a data-only contribution request, which doesnot require a recommendation or score.

In an example, the life occurrence management platform may collect datafrom all wallet types via API-based checkout requests to provide it tothe RBD platform as a contribution or recommendation request. In anexample, the life occurrence management platform may determine whetherto invoke step-up authentication based on different preferences. Thelife occurrence management platform may create ability for a wallet tobe flagged for example as Pre-authenticated and the like. Based on RBDrecommendation or based on defined criteria, step up authentication maybe bypassed. Otherwise if the RBD recommendation is bad, the lifeoccurrence management platform may not continue with user flow andfollow recovery path defined by each user experience. If RBDrecommendation is a challenge, the life occurrence management platformmay determine what step-up method may be presented to the user. If thelife occurrence management platform does not receive a response from theRBD system that is sufficiently high enough for authentication, the lifeoccurrence management platform may set default to challenge response. Inan example, the life occurrence management platform may follow specialstep-up rules when wallet is pre-authenticated and the card issuer issame as wallet issuer or other preset conditions. The life occurrencemanagement platform may maintain rules to determine which step upauthentication method to use in different situations and userpreferences. The life occurrence management platform may send data to aserver to generate an Account Address Verification (AAV) fortransactions. Authorizations may be appropriately flagged by the lifeoccurrence management platform to indicate level of, and reason for,authentication. The life occurrence management platform may wantauthentication and transaction data to be collected so that reporting,billing, and analytics capabilities are improved.

The life occurrence management platform may store billing and trackingdata. The life occurrence management platform may have data for trackingand to calculate possible future billing events which may made availableto a billing system and/or to a customer reporting system and/or and toan internal reporting system. This data may comprise data for eachprocessed transaction such as date and time, type of checkout used,Merchant ID, Wallet ID, PAN (may be kept encrypted for securityreasons—and only made available on a need to have basis), transactionamount (in USD plus EUR/BRL, depending on the issuer country), Cardbrand (MasterCard/Maestro/Other), Card type(Debit/Charge-or-Credit/Prepaid/not known), Issuer configuration optionsthat are applied to a transaction, Merchant configuration options thatare applied to a transaction (Basic, Verified, Advanced), step-up actiontaken (no step-up without valid Account Address Verification (AAV); nostep-up with valid AAV; step-up; not applicable), step-up result (AAVreceived; no AAV received; not applicable), transaction risk level for atransaction, type of post-back received (successful, not successful,none), Check-out phase reached by a transaction (successful login; cardselected; sent to 3DS; control handed back to merchant; post-backreceived), and the like. The life occurrence management platform may beable to provide check out quality data to Issuers, including failedauthentication rate of 3D secure, failed authentication rate ofcheck-out and the key components of such rate that may be calculated invarious ways, failed authentication rate of verified check-out, failedauthentication rate of basic check-out, fraud-related authorizationdecline rate, and the like. The life occurrence management platform maybe able to provide general data to issuers and may provide data tomerchants.

The methods and systems disclosed herein may comprise an ecosystemenabled lifestyle container. The lifestyle container may be implementedvia a mobile device. The lifestyle container may allow a user tofacilitate coordinating detection of triggers, use/execution ofpersonalized widgets, presentation of resolution actions, and executionof preconfigured mobile transactions to facilitate addressing a lifeoccurrence. Context for triggers may be developed based on time,location, transaction analytics, third-party user-related data such associal networks, calendars, family associations, etc. and adapting thecontainer to monitor trigger context for detection of triggers. Thetrigger context may be updated through an enabling layer operable on themobile device that provides access to trigger context sources such asGPS, clock, calendar, among others. The lifestyle container may comprisepersonalized widgets available to the eco-system that may be identifiedand configured to facilitate service delivery to address certain lifeoccurrences. The lifestyle container may be preconfigured with mobiletransactions that may be executed via the configured personalizedwidgets to effect service delivery that satisfies an aspect of the lifeoccurrence. Such preconfigured mobile transactions may be associatedwith resolution actions presented to the user in response to a detectedtrigger. Users may then accept and execute the transactions or actions.The lifestyle container may communicate with the MTP or the expertengine to maintain current context for triggers. Trigger context may besynchronized to maintain current context for the triggers.Synchronization may include updating the trigger context of thelifestyle container through an enabling layer operable on the mobiledevice that provides access to trigger context sources.

FIG. 8 depicts a high-level system diagram including a life occurrencecontainer configured to facilitate execution of mobile transactions forsatisfying an aspect of a life occurrence. The life occurrence node 120may be a user's mobile device or any other network connected device.When a life occurrence node 120 indicates a transaction is required tosatisfy an aspect of a life occurrence, the MTP expert engine 104 mayidentify merchants, determine coupons, utilize loyalty points andperform the transaction. The MTP 102 may facilitate a secure, enabledecosystem, with personalized transactions involving multiple providers,and disparate domains.

The life occurrence node 120 may be configured to include a lifestylecontainer 156 for facilitating transactions for satisfying an aspect ofa life occurrence in the secure mobile transaction platform. Thelifestyle container 408 may include widgets 802, electronic wallet 804,resolution actions 808, context monitor 810, trigger event detector 812,and an enabling layer 814. The trigger-events detector 812 may detectone or more trigger-events associated with a life occurrence. Thetrigger-events may be temporal based trigger-events, spatial basedtrigger-events, or other types of trigger-events. The temporal basedtrigger-events can be explicit in nature (e.g., user may define suchtrigger-events) or may be implicit in nature (e.g., the trigger-eventsdetector 812 may detect such time based events from the database, fromsocial networking sites associated with the user, and the like). Thelocation-based trigger-events may get activated when a life occurrencenode associated with the user is detected in specific spatial location.

The MTP expert engine 104 may include a context generator 816 that maydevelop context for the trigger-events based on time, space [location],transaction analytics, third-party user-related data [social n/w,calendar, associations (e.g. family), etc]. The context monitor 810 ofthe lifestyle container 408 may monitor the trigger-events context fordetection of trigger-events. Subsequently, the lifestyle container 408may facilitate updating of the trigger-event context through theenabling layer 814 that may be operable on the mobile device thatprovides access to trigger-event context sources (GPS, CLOCK, CALENDAR,etc.). The lifestyle container 408 may further identify and configurethe widgets 802 that may be available in the eco-system that facilitateservice delivery for addressing the life occurrence. Accordingly,lifestyle container 408 may associate resolution actions 808 that may bepresented to the user in response to a detected trigger-event withpreconfigured mobile transactions for executing the actions in responseto a user acceptance of the actions.

The MTP 102 may include a pre-configuring module 818 that maypre-configure mobile transactions that may be executed via theconfigured personalized widgets to effect service delivery thatsatisfies an aspect of the life occurrence.

The life occurrence may be predicted based on user-specific mobiletransactions processed through a mobile transaction platform anduser-related data derived from third party user data sources.Accordingly, the widget 802 may facilitate service delivery via aservice layer of a platform for secure personalized mobile transactions.Such personalized mobile transactions may be associated with a vendorthat may be determined from analysis of mobile transactions processedthrough a mobile transaction platform, life occurrence metadata, anduser-related data derived from third party user data sources. Theenabling layer 814 operable on the lifestyle container 408 mayfacilitate interoperation of the lifestyle container 408 and the lifeoccurrence node 120 (e.g. mobile device) resources including userinterface, communications, and secure element access.

An eco-system enabled lifestyle container may be configured for a userof a mobile device to facilitate coordinating detection of triggers foraddressing a life occurrence determined by an expert engine via uses ofthe mobile device. Context for developing triggers may include triggersbased on time, space [location], transaction analytics, third-partyuser-related data [social n/w, calendar, associations (e.g. family),etc.]. The container may be adapted to monitor trigger context fordetection of triggers. The lifestyle container may be synchronized withat least one of the expert engine and a mobile transaction platform(MTP) through which a user conducts transactions via the mobile deviceto maintain current context for the triggers. Synchronizing may includeupdating the trigger context of the lifestyle container through anenabling layer operable on the mobile device that provides access totrigger context sources [GPS, CLOCK, CALENDAR, etc.].

Configuring an eco-system enabled lifestyle container for a user of amobile device to facilitate coordinating detection of triggers foraddressing a life occurrence determined by an expert engine via uses ofthe mobile device may include a few steps. At least one step may includedeveloping context for triggers based on time, space [location],transaction analytics, third-party user-related data [social n/w,calendar, associations (e.g. family), etc.] and adapting the containerto monitor trigger context for detection of triggers. Another step mayinclude communicating among the lifestyle container, the expert engineand a mobile transaction platform (MTP) through which a user conductstransactions via the mobile device to maintain current context for thetriggers.

A lifestyle container as described above and elsewhere herein mayexecute on a mobile device in a way that is similar to a container thatis described in U.S. patent application Ser. No. 13/651,028 filed Oct.12, 2012.

Configuring the lifestyle container may include communicating over awireless signal (e.g. mobile network) between a mobile device thatstores the container and networked connected resources, such as othermobile devices, servers, point of sale devices, and the like.

The systems and methods disclosed herein may comprise a method ofalerting a user to a life occurrence. The creation of a life occurrencealerts may comprise taking metadata that describes a future lifeoccurrence. Possible resolution actions beneficial to take in advance ofthe occurrence of the life occurrence may then be determined based onmultidimensional context derived from analysis of user transactionsperformed with a mobile device via a mobile transaction platform andthird-party sources of user-related data. Additionally, context oftrigger conditions for each resolution action may be determined and thetrigger context monitored. When trigger conditions are met, theresolution actions may be presented. Such resolution actions may includelife occurrence context that is relevant to a user making a decisionabout accepting the resolution action. Each resulting action ortransaction for the contextual resolution action may be prepared on themobile device. When the user accepts a resolution action, the mobiledevice's action or transaction may be processed. Preparation of themobile device for the action or transaction may include configuring awidget to access an ecosystem service provider, an electronic wallet onthe user's mobile device, a secure element of the mobile device, and tooptionally trigger other widgets to execute on the mobile device.Additionally, preparation of the mobile device may include configuringone or more widgets that follow user preferences for form of payment,receipt handling, delivery/contact details, etc. to facilitate servicedelivery that effects the action/transaction without requiring userinput.

The systems and methods disclosed herein may comprise a token-basedmethod of life occurrence alert. The method may comprise taking metadatathat describes a future life occurrence and determining possibleresolution actions beneficial to take in advance of the occurrence ofthe life occurrence. Such a resolution actions may be determined basedon multidimensional context derived from analysis of user transactionsperformed with a mobile device via a mobile transaction platform andthird-party sources of user-related data. The method may comprisedetermining context of trigger conditions for each resolution action andmonitoring the trigger context. When trigger conditions are met, theuser may be presented resolution actions with appropriate context sothat the user may make a decision. The method may comprise preparing atoken to facilitate executing an action or transaction for eachresolution action and adapting the token based on context when the useraccepts a resolution action. The token may include metadata thatidentifies a transaction type accessible by a server anduser/wallet/device information required to execute the transaction onbehalf of the user.

The systems and methods disclosed herein may comprise a mobile deviceconfigured for life occurrence resolution. The device may comprise alifestyle container operable on a mobile device that coordinatesdetection of triggers, use/execution of personalized widgets,presentation of resolution actions, and execution of preconfiguredmobile transactions to facilitate addressing a life occurrence. Suchlife occurrences may be predicted based on user-specific mobiletransactions processed through a mobile transaction platform anduser-related data derived from third party user data sources. The devicemay comprise a personalized widget for facilitating service delivery.The personalized widget may facilitate such service delivery via aservice layer of a platform for secure personalized mobile transactions.The widget may be associated with a preconfigured mobile transactionvendor that is determined from analysis of mobile transactions processedthrough a mobile transaction platform, life occurrence metadata, anduser-related data derived from third party user data sources. Thelifestyle container may comprise an enabling layer operable on themobile device for facilitating interoperation of the lifestyle containerand mobile device resources including user interface, communications,secure element access. Additionally, the lifestyle container maycomprise an electronic wallet operable on the mobile device that thepersonalized widget is authorized to access for facilitating servicedelivery.

FIG. 9 depicts a high-level system diagram including a life occurrencecontainer that may facilitate coordinating detection of trigger-eventsfor addressing a life occurrence. In addition to functionalitiesdescription in FIG. 8, the life occurrence management platform maysynchronize the lifestyle container 408 with at least one of the MTPexpert engine 104 and the MTP 102 through which transactions areconducted on behalf of a user via the life occurrence node 120 (e.g. amobile device) to maintain current context for the trigger-events. Thesynchronizing may include updating the trigger-event context of the lifeoccurrence container through the enabling layer 814.

An eco-system enabled lifestyle container 408 may be configured tofacilitate coordinating detection of trigger-events for addressing alife occurrence determined by an expert engine via uses of a lifeoccurrence node, such as a mobile device. The context may be developedfor trigger-events based on time, space (e.g., location), transactionanalytics, third-party user-related data (e.g., social n/w, calendar,associations (e.g. family), etc), risk profiles and the lifestylecontainer 408 may be adapted to monitor trigger-event context fordetection of trigger-events. Further, the communication among the lifeoccurrence container, the expert engine and a mobile transactionplatform (MTP) may be enabled to maintain current context for thetrigger-events.

A method of life occurrence alert may include taking metadata that maydescribe a future potential life occurrence. Various possible resolutionactions that may be beneficial may be determined in advance of thefuture life occurrence. The resolution actions may be determined usingmulti-dimensional context that may be derived from analysis oftransactions performed on behalf of a user with the life occurrence node120 (e.g. a mobile device) via a mobile transaction platform andthird-party sources of user-related data. The context of trigger-eventconditions for each resolution action may be determined andtrigger-event context may be monitored. The method may present theresolution actions to the user when the trigger-event conditions aremet. Such resolution actions may include life occurrence contextrelevant to a user making a decision about accepting the resolutionaction. The method may prepare a mobile device action/transaction foreach resolution action and may adapt the mobile deviceaction/transaction based on action/transaction context when a resolutionaction is accepted by the user.

A widget may be configured to access an ecosystem service provider, anelectronic wallet on the user's mobile device, a secure element of themobile device, and to optionally trigger other widgets to execute on themobile device. Additionally, one or more widgets that follow userpreferences for form of payment, receipt handling, delivery/contactdetails, etc may be configured to facilitate service delivery thateffects the action/transaction without requiring user input.

An instrument-based method of life occurrence alert is disclosed. Themethod may include taking metadata describing a potential lifeoccurrence and determining possible resolution actions beneficial totake in advance of the potential life occurrence. The possibleresolution actions may be based on multi-dimensional context derivedfrom analysis of user transactions performed with a mobile device via amobile transaction platform and third-party sources of user-relateddata. The method may determine the context of trigger-event conditionsfor each resolution action and monitor trigger-event context. Further,the method may include presenting the resolution actions whentrigger-event conditions are met. Such resolution actions may includelife occurrence context relevant to a user making a decision aboutaccepting the resolution action. Further, the method may includepreparing an instrument to facilitate executing an action/transactionfor each resolution action and adapt the instrument based on contextwhen a resolution action is accepted by the user. The instrument mayinclude metadata that may identify a transaction type accessible by aserver and user/wallet/device information required to execute thetransaction on behalf of the user.

The life occurrence management platform may facilitate interactions withthe user through various user interfaces that are configured to displaya plurality of moving panels for performing different trigger actions.Such user interfaces and the moving panels are discussed herein inconjunction with various figures related to the user interfacesassociated with a life occurrence management platform. In an example,the user interfaces may be presented on a mobile device, cell phone, ona washing machine display panel. In other examples no user interfaces onlife occurrence devices at all and a life occurrence management platformmay simply run a series of transactions and settle wherever it needs tosettle.

A node or a mobile device may be used for triggering a transaction. Inother cases, a mobile phone with a lifestyle interface may be used totrigger the transaction. In some cases, the transaction triggering maybe done without any device at all. The node may be a, tablet device, amobile phone, or smart phone that may be configured for lifestyle mobileapplication for shopping or other life occurrences. In some cases, thenode may be any other networked device such as a washing machine thatmay be connected with a secure, enabled ecosystem of payment providers,service providers, and the like.

A life occurrence management platform may be implemented through alifestyle layer that may service the nodes including an endpoint devicethat may order things as well as act as an application on the node. Forexample, the nodes may be capable of performing shopping tasks. Thesenodes may comprise a washing machine, mobile phone, vending machine, orany other networked device. For example, if kids are returning fromvacation, a person may need more supplies and the washing machine mayalso require more detergents. Therefore, based on the contextualinformation, the vending machine or washing machine may order moresupplies accordingly based on derived contextual information about theuser and his kids.

In a healthcare related example, the life occurrence management platformmay facilitate refilling of medications based on information fromsensors as end nodes. The life occurrence management platform mayfacilitate in purchasing pills for a user or manage hospital experiencessuch as including without limitations annual checkup, taking care ofco-pay, insurance, etc. through the MTP. In an example, sensors may beprovided with the nodes that may take signals e.g., embedded signalsthat may send out signals constantly, e.g., insulin levels for diabeticsand the like. The sensors may be FDA approved sensors emanating signals.The life occurrence management platform may get the signals and guideroutine hospital experiences accordingly. A life occurrence managementplatform may provide lifestyle experience on a user's phone such aswalking him through whole hospital process and guiding him through theroute etc. for the hospital and about parking arrangements and prepaidcard that the user may use in the hospital and the like.

The life occurrence management platform may facilitate in applicationssuch as a diet application that may constantly monitor dietaryconditions, and may include consumer lifestyle utilities and the likewherein the user may log what he may eat. The logged information may beretrieved by a life occurrence management platform so as to take care ofthe healthcare and dietary aspects of the user. A user's FITNESS PAL™may further take manual entry of calories, etc and the caloriesinformation may be used by a life occurrence management platform.

Utility/Usability

An entire transaction spectrum, starting from providers that are part ofthe secure enabled ecosystem, through the life occurrence managementplatform infrastructure, to the nodal devices, may be separated into aset of capabilities that may form an overall Utility, and, a set ofcapabilities that may form Usability. A life occurrence managementfunctional layer may be provided that may intervene between the nodalpoints and the entire secure enabled ecosystem. A part of a lifeoccurrence management platform may interact with the secure enabledecosystem. Another portion of a life occurrence management platform mayinteract with the nodes. A life occurrence management platform may beenabled through several layers. These may include a utility layer and ausability layer. The utility layer may be enabled by the devices thatmay have capabilities that provide utility for conducting securetransactions. The utility layer may represent various portions of thelife occurrence management platform as modular infrastructure elementsof a utility. The various modules may be presented as infrastructureservices sets that may be presented as utilities upon which applicationsmay be made. The usability layer may be enabled through applicationsthat may provide a layer on the usability side.

The utility and usability as discussed herein may be defined as tiers ofcapabilities, wherein at one end of the tiers will be features orbuilding blocks that may comprise user, device, and applicationagnostic, and, at the other end may be capabilities which may be highlycustomized and personalized for users, devices, and applications and thelike. The aspects of usability and utility may facilitate in buildingnecessary linkages among various modules of a life occurrence managementplatform and their coupling for efficiency and scalability. It must beappreciated that utility components may be evolved and governed overtime whereas usability components may be best suited to a relativelyunrestricted and open development environment of a life occurrencemanagement platform. It must be appreciated that certain features orcapabilities of a life occurrence management platform may be part of autility-tier, and certain features or capabilities may be part of aUsability-tier.

FIGS. 10-16 depict various tables that may include a plurality of datatypes and corresponding attributes of the data types that may besuitable for use in a multi-dimensional context-based database. Theplurality of the data types may be indicative of the respectivedimensions of the multi-dimensional database 118. The data of themulti-dimensional database 114 may be stored within the plurality ofdata tables as described herein. A table 1 depicts a category table thatmay be utilized for storing information associated with the type of theproducts for which the transaction may be carried out on detection ofthe life occurrence. The category table 1 may include category ID, nameof the category and status of the category. A sub-category for each ofthe category may be utilized for storing information associated with theproducts corresponding to the sub-category of the main category. A table2 depicts an exemplary embodiment of the sub-category table. Forexample, an electronics category in the table 1 includes twosub-categories, namely phone and laptops as depicted in the table 2.

A table 3 depicts a merchant table that may be configured to storeinformation associated with the merchants that may be available to theuser in the mobile transaction platform. The table 3 may be used forstoring information such as a merchant ID, name of the merchant, typesof the services being offered by the merchant, web address for themerchant websites, any image associated with the merchant and otherrelated information. Such information may be displayed to the user ormay be used by the MTP expert engine 104 so as to select a specificmerchant from the plurality of stored merchants.

A table 4 depicts an example of a product table that isgenerated/referenced when performing a comparative shopping activitywith lifestyle. The product table may be utilized for storinginformation such as product ID, product name, model number, sub categoryassociated with the product (e.g., as indicated in table 2, informationrelated to manufacturer, manufacturing date, price and other informationassociated with the product. A table 5 depicts an example of aninventory table for the products that be offered to the user whileperforming a shopping activity with the lifestyle container. Theinventory table may be used to store information such as validity periodof the offer, specifics of the offer, minimum adjustment amount of theoffer, discount percentage for the user, available quantity and thestatus of the available offers. The MTP expert engine 104 may use thesedata so as to determine an offer to the user while generating aresolution path for the life occurrence.

A table 6 may include the example of plurality of units that the MTPexpert engine 104 may support while determining the type of lifeoccurrences and for generating a resolution path for resolving one ormore aspects of the life occurrences. A table 7 depicts an example of anevent table that may have been learnt by the MTP expert engine 104 so asto determine a candidate resolution action for resolving the one or moreaspects of the life occurrences the life occurrence. The event table mayinclude events such as a wedding date, birthday date, graduationceremony, valentine day and other important days or time on occurrenceof which the user may like to perform an action using the lifestylecontainer.

A table 8 depicts an example product offer table, which may be availableto the user so as to perform shopping for the products available in theproduct offer table. A table 9 depicts an example of a loyalty tablethat may be generated for a specific user depending on the type of theuser. The MTP expert engine 104 may determine the user behavior andanalyze the transactions of the user to generate the loyalty offer forthe user. The user register himself for the loyalty offers so that ondetection of the life occurrence the MTP expert engine 104 may suggest abest possible loyalty offer to the user.

A table 10 is an example of a store message table, which may be used forstoring messages for a specific merchant. The store message table mayinclude information that may be harvested by, learned by, and/or pushedto the MTP expert engine 104 for use in the multi-dimensional contextand resolution actions. A table 11 is an example of a product suggestiontable that may store information associated with the suggestion forallowing mapping of one or more compatible products with a particularproduct. A table 12 is an example of a merchant _user table that may beused for storing information such as loyalty of the user for a specificmerchant, any corresponding loyalty card related information, andmerchant related information. The MTP expert engine 104 may utilize thetable to suggest offers to the users depending on the loyalty of theuser for the specific merchant.

A table 13 is an example user behavior table that may be indicative ofthe behavior of the user on the mobile transaction platform. The MTPexpert engine 104 may be configured to analyze the user behaviors andmake predictions for the patterns for the user using machine learningtechniques, fuzzy logic or neural networks. Further, a table 14 is anexample of initial behavior −1 for the user that may include list oflife occurrences of the user, which need to be managed. Similarly, atable 15 is an example of other version of the behavior of the user. TheMTP expert engine 104 may be configured to perform the behavior analysisof the user so that the MTP expert engine 104 may be learn the userengagement tendencies. Such type of behavior analysis may enable the MTPexpert engine 104 to provide better grading of results that may bepresented to the user. A table 16 is an example of a user alert tablethat may be generated for preparing a dynamic list of user lifestyleoccurrence specific alerts. For example, when a user is planning totravel, the MTP expert engine 104 may determine the status of the flightof the user and accordingly, may alert the user.

The multi-dimensional database 118 may be configured to store variousforms of the data in the form of tables. For example, a table 17 is anexample of a payment card table that may include information associatedwith the payment card of the user. The payment card table may includeinformation regarding the wallet ID that may be associated with thespecific payments card. When the user may perform any transaction withthe wallet, the user may select the associated payment card. Themulti-dimensional database 114 may include a store table 18 that mayinclude information associated with the available stores andcorresponding merchants. The multi-dimensional database 118 may includea table 19 for storing address related information for the user. Forexample, for each user ID, a shipping address may be stored so that theMTP expert engine 104 may complete the shopping transaction and maydispatch the products to the shipping address as stored in the addresstable of the user.

The multi-dimensional database 114 may include transaction table 20 forstoring information corresponding to the past transactions of the user.The transaction table 20 may include information corresponding to walletID, transaction ID, point of sale terminal ID, merchant ID and otherrelated information. A table 21 may be used for transaction objectrelated information for each transaction. The transaction table 20 andthe transaction object table 21 may be used by the MTP expert engine 104to determine transaction behavior analysis of the user so as to generatea resolution path for the one or more aspects of the life occurrence.

The multi-dimensional database 118 may include other information such asdata related to inventory of the products associated with the merchantin an inventory table 22, user profile related information in a userprofile table 23 and widget related information in the widget table 24.

FIG. 17 illustrates an example embodiment of a method for facilitatingon boarding of a user. At step 1702, the user may onboard the lifestylecontainer to accept the initial terms and conditions. The lifestylecontainer may be closed when the user may reject the terms andconditions required to access the lifestyle container to perform one ormore transactions. Otherwise, the details such as a mobile number, anemail ID, and other user related information might be added. Further,various intelligent appliances such as washing machine, vending machineand the like may be registered with the lifestyle container and thelifestyle container is registered with the MTP 102.

At step 1704, a determination is made as to whether the lifestylecontainer requires additional processing. The MTP expert engine 104 mayapply rules and logic to the received content when the lifestylecontainer may require additional processing. Otherwise, informationassociated with the lifestyle container may be persisted and stored inthe multi-dimensional database 118. At step 1708, the method may includedetermining requirement of connecting with other systems. The MTP expertengine 104 may connect with the other networks/infrastructure via theenterprise service bus and information such as user profile fromexternal sites, shopping history from merchants, travel information;health related information and social networking profile of the usermight be retrieved. The retrieved information may be sent back to theMTP 102 for further processing.

At step 1710, the MTP expert engine 104 may process the content receivedfrom the external networks or from the internal database and generate auser profile at the MTP expert engine 104. The MTP expert engine 104 maycommunicate the profile generation information with the MTP 102 via MTPEE interfaces and subsequently, the MTP 102 may mark the user as aregistered user with the system. At step 1712, a message correspondingto a successful registration of the user may be displayed on a displayscreen of the life occurrence node 120 such as the mobile device of theuser.

At step 1714, the enabled ecosystem may generate offers, notificationsand messages for the user to the MTP 102 via the ESB 124. Subsequently,the MTP 102 and the MTP expert engine 104 may processes these offers,notification and messages in accordance with profile of the user. Atstep 1718, the processed offers, notification and messages may be sentto the lifestyle container either through push messages or throughsynchronization methods. Subsequently, the offers, notification andmessages may be displayed for the user.

Shopping Flow

FIG. 18 depicts an example embodiment of method for facilitatingshopping transaction for the user in the mobile transaction platform.The method may include accessing the lifestyle container for initiatingthe transaction on determining the life occurrences. The user may havethe lifestyle container on a life occurrence node 120 such as the mobiledevice as a lifestyle app. In another example, a washing machine or anyother utility device might also be set up for the lifestyle ecosystem.At step 1802, the lifestyle container may facilitate the user to addmanually the shopping list and send the shopping list to the MTP 102.The lifestyle container may allow the user to tag the earlier savedoffers in the shopping list and send the saved offers as items of theshopping list to MTP 102.

At step 1804, a trigger for the shopping list is generated and sent tothe MTP expert engine 104. The trigger may be a time-based trigger or alocation based trigger. For example, on detection of arrival of an event(e.g., birthday event), a time-based trigger is generated. In anexample, Facebook birthday event may trigger possible birthday shoppingand birthday itself may trigger a shopping flow. A Facebook applicationmay remember that the user bought a tie for a friend last year, so itcould inject a past purchase (cufflink) into the shopping flow to searchfor something else. The usability component may inject past history intothe utility component at that time. The life occurrence managementplatform may collect information from third party data such as that theuser's friend returned the tie last year. In another example, dependingon the location of the user, a location-based trigger may be generated.At step 1808, the triggers thus generated may be sent to the MTP expertengine 104.

At step 1810, the MTP expert engine 104 may be configured to access theknowledge database and list of the triggers. The knowledge database mayinclude initial behavior of the user so that the MTP expert engine 104may utilize the behavior of the user to generate the shopping list thatmay be sent to the merchants. The behavior may include a plurality ofshopping preferences such as sizes for example waist, dress, shoe, sizesand the like, brands, styles, genres, loyalty information, and the like.Other shopping attributes that may be extracted from the behavioranalysis of the user may include merchant specific attributes acrossvarious merchants, transactions-related attributes including of otherusers, and the like.

For example, when a user goes to a merchant (couponing system) and hebuys something, the user may get a coupon from the back end/cloud thatmay get pushed to the user. The user may get an instrument (coupon) as aresult of that transaction. In such scenario, pushing out a coupon to auser's wallet might be a natural example in a life occurrence managementplatform. The coupon may be designed for anything. For example, if theuser buys toothpaste, he instantly may get a coupon for a toothbrush. Ifevery Friday Bob buys bread, the user might send a coupon from thecouponing system, transfer it to the MTP and the MTP may push out thecoupon to the user's wallet. The MTP expert engine 104 may recognizecharity and coupons as a result of transactions but the user might wantto generate a coupon based on a life occurrence analysis. For example, auser may buy bread on Fridays, so the life occurrence managementplatform may push a coupon on Thursday usable for buying the bread. Thelife occurrence management platform pushes coupons and the like thatcomplements a transaction. The MTP expert engine 104 may for exampleintercept a coupon and know whether the user really needs a toothbrush.The life occurrence management platform may shop it around or find outwhether that is the right coupon or whether the user needs somethingelse. The life occurrence management platform finds out if the user isentitled to a discount on something he already bought.

The shopping list generated for the merchants may be sent to themerchants through the enterprise service bus. At step 1812, the enabledecosystem may facilitate the merchants to add best offers/prices to theshopping list and may send the shopping list tagged with the best offersto the MTP expert engine 104. The life occurrence management platformmay apply merchant rules. The rules may be user-dependent, may be basedon a type of user, volume requirements e.g., across multiple users, andthe like. An example of a merchant rule may be that Costco takes Amexbut no other cards. If Costco is the best place to fulfill a user'scomplete order, then the user may not use his United MasterCard or othercard. If the user uses a particular merchant like Costco, he can't usehis MasterCard. The consumer might want to know what is going on beforeshopping at a place that won't take his card. Another example may bethat Wal-Mart got customers to use PIN/Debit instead of credit cards.Merchant rules may be derived from the enabled ecosystem. They may bederived in through the Enterprise Service Bus through an API that allowsspecifying rules, conditions, etc. so that the expert engine may consumethose rules. Merchant rules may be consulted when a bid is placed to seewhat rules apply to what that bid.

At step 1814, the MTP expert engine 104 may process the shopping listincluding the offers from the merchants to the MTP 102 and further tothe lifestyle container for presenting the shopping list to the user. Atstep 1816, the user may decide to go with the shopping list and at step1818, the shopping list is sent to the merchant via the ESB so that themerchant may process the shopping list. The processing of the shoppinglist may include generating the receipt and packaging the final order.The receipt is transmitted to the MTP 102 via the ESB and further to theuser whereas, the order may be dispatched to the user. The MTP expertengine 104 may access the shopping list that may be processed by themerchant and may apply rules and fuzzy logic to build the user behavior.

In an example, shopping types may include just finding matching items,comparison shopping, Bidding, Reverse auction, expert using an expertshopping engine that may shop price line, hotwire and others. The lifeoccurrence management platform may facilitate applying for any offersand coupons in a user's repository through lifestyle container on user'swallet or otherwise in his data repository. The life occurrencemanagement platform may consult rules for the user and for example linkthem to the rules engine/expert engine). The expert engine may applylearning stuff to figure out what bids are the best for a user. Theexpert engine may for example perform refinement of a bid because itsenses from the user behavior that the user is not using cards at targetanymore. The life occurrence management platform may consult rulesregarding extent of desired intervention. A user may dial up or dialdown the degree of intervention. The dial-up/dial-down may be based onsectors such as control in health care may be more relative to shopping.Within each sector, the life occurrence management platform may offerdifferent sets of interventions. The life occurrence management platformmay offer granular control for some cases and automated intervention forothers. The expert engine may apply rules to general bids. For example,the life occurrence management platform may apply the rules to responsesfrom step of shopping the shopping list. The rules engine may beconnected with a fuzzy system. The rules engine may serve as a learningengine that may learn for example that someone likes a certain thing ina given circumstance.

Parking Flow

FIG. 19 depicts an example embodiment of a method for facilitatingparking arrangements for the user in response to a life occurrence. Atstep 1902, the method may include identifying a location at which theuser may be interested for discovering a parking space. The user may addappointment related information in the calendar of the lifestylecontainer. The lifestyle container may be configured to detect thetiming and location from the calendar application so that the parkingarrangement may be made for the user. The lifestyle container maydetermine the timing and location information from the travel bookingsthat may have been done by the user. FIG. 19 illustrates an exampleembodiment in which the life occurrence management platform may identifythe timing and location required for making the parking arrangementsfrom the calendar application. As depicted, the calendar available atthe lifestyle container is synchronized with the cloud calendaravailable at the MTP 102. The cloud calendar available at the MTP 102may also include information from the company calendar and the travelbooking related information received from the enabled ecosystem.

At step 1904, the MTP 102 may share the cloud calendar with the MTPexpert engine 104 so that the MTP expert engine 104 may process it togenerate a parking list. The MTP expert engine 104 may be configured todetermine user behavior that may be available in the knowledge databaseaccessible to the MTP expert engine 104. The knowledge database may bethe multi-dimensional database 114 as explained earlier in thedescription. The MTP expert engine 104 may determine usual preferencesfor parking such as vehicular based parking, parking prices and the likefrom the user behavior.

At step 1908, the MTP expert engine 104 may share the processed parkinglist with the parking providers through the enterprise service bus. Atstep 1910, a determination is made as to whether the parking providerssupport the pre-booking. At step 1912, a parking ticket may be generatedwhen parking provider supports the pre-booking and the parking providermay transmit the parking ticket to the MTP expert engine 104. At step1914, the MTP expert engine 104 may process the parking ticket with thefuzzy logic or neural network so as to update the user behavior in theknowledge database. Subsequently, the MTP expert engine 104 may transmitthe parking ticket to MTP 102 that may push the parking ticket to theuser.

Otherwise, at step 1918, the parking providers may generate a referencenumber when the parking providers may not provide a pre-booking. Thereference number may be forwarded to the MTP expert engine 104 that maysave the reference to provide live feed to the user on availability ofparking and best available prices. The MTP 102 may send the reference idto the enabled ecosystem when the user has started journey and may nothave the pre-booked ticket. The live feed may be pushed to the userdevice and the user may take action on these live feeds depending on therequirement.

As described more fully below, a system comprises a mobile transactionplatform (MTP) in communication with a plurality of service providersand one or more containers operating on a mobile device. The MTP isconfigured to facilitate mobile transactions between the one or morecontainers and the plurality of service providers and to derive analyticdata from the mobile transactions. The one or more containers mayinclude a lifestyle container as described in further detail laterherein. An expert engine is in communication with a plurality of sourcesof third party user data and the MTP and is configured to consolidatethe analytic data and the plurality of sources of third party user datato create a multidimensional context for determining life occurrencesand resolution paths for resolving aspects of a life occurrence.

The resulting system 100 enables a user experience through the mobiledevice by relying on a lifestyle container 408 that facilitatespresenting notifications of triggered life occurrences derived from arobust multidimensional context with associated consolidated resolutionactions. Such resolution actions may include, for example, at least onesecure mobile transaction and collectively serve to guide a user througha series of choices to resolve the triggered occurrence. In this way, atriggered life occurrence event may drive the transactions thatdetermine the user experience. As described more fully below, the MTP102 aggregates disparate domains and attends to the complexities ofsecure transactions comprising components of the resolution actions. Theexpert engine consolidates numerous sources of analytics to create andmaintain the multidimensional user specific context that drives thederivation of the life occurrence-related triggers.

With reference to FIG. 20, there is illustrated a system 2000 accordingto an exemplary and non-limiting embodiment. The Mobile TransactionPlatform (MTP) 102 as discussed in conjunction with various figuresalready operates, generally, to facilitate communication between theexternal entities 122 and the lifestyle container 408. Examples of theexternal entities 122 include, but are not limited to networks andgateways, offers and value added services (VAS), other host systems,Trust Service Managers (TSMs) and Certificate Authorities (CAs),user/host databases, and the like. In accordance with an exemplary andnon-limiting embodiment, the MTP 102 communicates with the externalentities 104 via the enterprise service bus (ESB) 124. While the MTP 102operates to facilitate mobile transactions between the external entities122 and the lifestyle container 408, it facilitates passing data betweenthe external entities 122 and the lifestyle container 408 withoutsubstantively altering their content. However, the MTP 102 does acquireand collect for storage in, for example, the transactional analyticsdatabase 128, information and metadata related to various attributes ofthe transactions enabled by the MTP 102. For example, the MTP 102 maystore in the transactional analytics database 128 information related totransaction times, transaction amounts, service provider identifiers,life occurrence-related trigger, user action(s) to effect thetransaction, and the like.

In communication with the MTP 102, the expert engine 104 operates toconsolidate various transactional analytics received from the MTP 102with one or more of the third party sources 130 to create amultidimensional context that is suitable for determining lifeoccurrences, developing and maintaining occurrence action triggers,generating resolution paths that resolve an aspect of a life occurrencevia uses of the mobile device, and the like. Examples of the third partysources 130 include, but are not limited to, third party analytics,social networks and various other context drivers examples of which aredescribed more fully below. In accordance with an exemplary andnon-limiting embodiment, the expert engine 108 communicates with thethird party sources 130 via the ESB 124. As described more fully below,the expert engine 104 may employ a feedback system operating between,for example, a fuzzy system 204 or rules engine and a neural network208. In operation, the expert engine 104 may employ one or morealgorithms to consolidate various transactional analytics from the MTP102 with data from the third party sources 130 to produce amultidimensional context from which triggers may be produced. Suchalgorithms may further order and prioritize the display of lifeoccurrence-related alerts to a user of the mobile device.

As described more fully below, the expert engine 104 makes use ofvarious context drivers to create the multidimensional context includingpast transactions, learning from preferences of a user, the presence ofa network or a particular account, the presence of vouchers andpromotions, loyalty points and the like.

In some embodiments, the multidimensional context may comprise at leastone of user or life occurrence location information. For example, thecurrent location of the user or the life occurrences may be determinedusing any location determination technologies such as global positioningsystem (GPS) and the like. The multidimensional context may comprise atleast one of time of life occurrence and current time.

The data derived from the transactional analytics database 128 may beincorporated into a static user profile and a dynamic profile of theuser for use by the MTP expert engine 104. In some embodiments, thetransaction data may be analyzed by the MTP 102 in context of otherusers, similar or interested vendors, etc. to establish some sort ofweighting, importance, etc. This analysis may result in determination ofa new trigger, action, or occurrence. The transactional analyticsdatabase 128 and the MTP expert engine 104 may exchange resolutiontriggers, static user profiles, and dynamic user profiles. The staticuser profiles may be used in conjunction with current context data suchas time, space, and user input for the MTP expert engine 104 in order todetermine life occurrences.

With reference to FIG. 21, there is illustrated a system 2100 accordingto an exemplary and non-limiting embodiment. The Mobile TransactionPlatform (MTP) 102 operates, generally, to facilitate communicationbetween the external entities 122 and the lifestyle container 408. Inaccordance with an exemplary and non-limiting embodiment, the MTP 102communicates with the external entities 122 via the enterprise servicebus (ESB) 124. Among other elements and components, the system 2100 mayinclude a switch 2102. The switch 14A02 may facilitate access to theecosystem resources such as third party analytics, social networks,context drivers and at least one of networks and gateways, offers andvalue added services, host systems, trusted service managers (TSM),certificate authorities (CA), and databases for the MTP expert engine104 and the transactional analytics facility 128. The switch 2102 may beconfigured to access transactional components in the ecosystem forfacilitating financial transactions through for example prepaid cardsamong users, service providers, billing agents, and financial servicesagents and the like. For example, the switch 2102 may be communicativelyconnected with the transactional analytics 128 for facilitating suchtransactions among various entities to provide a user-centricexperience. The switch 2102 may be accessible through the lifestylecontainer 408 deployable on a node such as a mobile device and the like.

The system 2100 may further include a risk scoring module 2104 thatgenerates a risk score that may be utilized as a context and decisiondriver to determine if one or more resolution actions are suitable forpresenting to a user. The risk based decisioning is an expert processand could be carried out at either the level of expert engine 108 or atthe level of enterprise service bus 124. In accordance with theillustrated embodiment, the risk scoring module 2104 may operate inassociation with the transactional analytics facility 128 for analyzinguser transactions associated with the mobile transaction platform 102and third-party sources of user-related data to generate a risk profileof users, trigger-events, third-parties, resolution actions, lifeoccurrences, potential transactions, and the like based onmultidimensional live occurrence context. In an example, the riskprofile may be used to determine if one or more resolution actions aresuitable for presenting to a user. In an example, the risk profile isuseful to rank resolution actions. Based on risk calculations by therisk scoring module 2104, the expert engine 104 may determine lifeoccurrences and suggest resolutions based on multidimensional contextderived from analysis of associated risks in connection with third partysources and the like. In an example, the risk scoring module 2104 mayfacilitate maximize checkout conversion rates and decrease fraud ontransactions through use of a risk based authentication system anddynamic multi-factor authentication methods. A user may be able tochoose if he wishes what type of Checkout he may want to use duringAccount Management. The system may re-use existing functionality formerchants to opt into advanced checkout (Direct and BMU) and onboard 3DSinformation. As part of account management, merchants may be able to optin to different levels of authentication. In an example, the system 2100may assign a confidence interval to indicate if account owner is likelya fraudster or a legitimate customer and normalize it as a generic valuefor the RBD.

In accordance with exemplary and non-limiting embodiments, the expertengine 104 may utilize risk as a context driver when generating triggersand attendant resolution actions. For example, as the MTP 102 executesone or more transactions in response to a user's inputs in response toan alert of a trigger, the MTP 102 may dynamically identify one or moreattributes of the transactions as amounting to an unacceptable risk. Inresponse, the MTP 102 may alert the user to, for example, chose adifferent mode of payment or another vendor. For example, a user may beprovided multiple payment options for proceeding with the mobiletransaction. The user may further have defined a default credit card formobile transactions that may be selected in the event that no other formof payment is selected and/or if a chosen form of payment is notacceptable. In this way, the MTP, in cooperation with the lifestylecontainer 408 and/or other MTP resources on the mobile device, mayautomatically switch forms of payment and/or vendors in response todetection of an unacceptable level of risk based on the risk scoregenerated by risk scoring module 2104. A non-limiting example of riskmanagement, the user may buy neckties and the expert engine 104 mayidentify matching cufflinks and suggest to the user to purchase thematching cufflinks at an identified vendor with the vendor's issuedcredit card. The user agrees and the lifestyle container 408 is updatedto facilitate presenting a consolidated view of the transaction.However, the expert engine 104 determines that an aspect of risk of thistransaction is unacceptable (e.g. payment terms of the vendor's creditcard are onerous) and suggests to the user the choice of using adifferent credit card that is accessible in the user's mobile wallet viathe mobile device instead of the store card, even though the user willlose out on some vendor-specific loyalty points.

With reference to FIG. 22, there is illustrated a schematic diagram ofthe operation of the expert engine 104 according to an exemplary andnon-limiting embodiment. As illustrated, it is evident the similarity inthe manner in which the expert engine 104 handles temporal and spatialcontext drivers/data. With regard to time based actions, illustrated inthe block to the left of the expert engine 104, actions including thedisplay of notifications, alerts, suggestions and the like are takenbased, at least on part on the current time, a temporal window andinformation defined by the user and/or information from third partysystems such as, for example, social network sites. As illustrated inthe bock to the right of the expert engine 104, location basedinformation is treated in much the same manner. Alternatively, theexpert engine 108 may handle temporal and spatial context drivers/datadissimilarly.

In general, the mobile transaction platform 102 may receive user datafrom a source, such as the external entity 122, and such as via the ESB124. The MTP 102 may then transmit the user data to a user such as auser operating a mobile device executing the lifestyle container 408.Next, the MTP 102 may derive a plurality of transactional analytics datafrom the transmitted user data which it may then transmit to the expertengine 104 which may be configured to consolidate the plurality oftransactional analytics data with at least one third party source ofuser data. As described more fully below, the MTP 102 may receive fromthe expert engine 104 one or more triggers derived from themultidimensional context wherein each trigger is triggered based on anoccurrence for which one or more resolution actions are provided to theuser of the lifestyle container 408 enabled mobile device.

An expert engine, such as the expert engine 104 depicted herein may bebased on a variety of known technologies including several technologiesco-owned by the applicants hereof. In U.S. patent application Ser. No.10/284,676 filed Oct. 31, 2002 that is incorporated herein by referencein its entirety, an expert system in context of a transactionenvironment is described. This expert system performs data consolidationfrom a variety of sources including direct client input, serviceinstitutions, merchants, vendors, government agencies, client profile,transaction records analysis, rules/flags, and the like for providing toa knowledge based system that effects services to clients. Servicesinclude among other things, matching services to connect users withproviders of services/products based on the user's request/interests(implicit and explicit), personal data, account data, and transactiondata; suggesting a transaction based on user personal, account, andtransaction data and a database of vendor/service provider information;and confidentially “negotiating” an offer on behalf of the user byrevealing some confidential information related to the specificnegotiation objective (e.g. frequent flyer miles/points details, size ofhousehold, etc.).

Another expert system embodiment is described in U.S. patent applicationSer. No. 11/539,024 filed Oct. 5, 2006, that is incorporated herein byreference in its entirety, an expert system in context to a transactionenvironment for secure mobile transactions is described. This expertsystem facilitates customizing the user interface experience anddetermining user preferences by operating over time using the “user'sbehavior, usage patterns, transaction history and qualified externalinputs”, particularly as depicted in FIG. 47; includes a learning enginethat may learn which services a user tends to use and push data to themobile device to improve delivery of those services; rules-basedoperation to handle prioritizing data flow and transactions based onpayment due date, payment “importance”, etc., managing applicationthroughput to improve user's access to data, and the like.

Yet another expert system embodiment is described in U.S. patentapplication Ser. No. 13/651,028 filed Oct. 12, 2012, that isincorporated herein by reference in its entirety, an expert system incontext of a mobile transaction platform for secure personalizedtransactions is described. This expert system facilitates delivering asimplified user experience, customization of service and personalizationelements, analyzing user habits; automatically adjusting the platformfeatures to present itself in the manner most suitable to individualusers including regional preferences (“most French like it this waywhile the Americans like it that way”), mobile device capabilities(screen size, keyboard available or only on-screen, and the like), anddifferences in client base (low-end versus luxury customers); carryingout analytics on the transactions, usage patterns, and other parametersthat will help the ‘learning’ process of an inference engine, and thelike.

Any of these expert systems may be capable of performing at leastportions of the methods and systems of life occurrence determination andresolution path generation as described herein. An expert system of U.S.patent application Ser. No. 10/284,676 may, among other things,facilitate data consolidation from a plurality of data sources includingmobile transactions performed in association with a transaction platformof the expert system. An expert system of U.S. patent application Ser.No. 11/539,024 filed Oct. 5, 2006 may, among other things, facilitategenerating aspects of a multidimensional context that is suitable forlife occurrence determination based at least in part on analysis overtime of a user's behavior, usage patterns, transaction history and thelike. An expert system of U.S. patent application Ser. No. 13/651,028filed Oct. 12, 2012 may, among other things, provide context for asimplified and improved user experience, such as by analyzing userhabits, carrying out analytics on transactions, and automaticallyadjusting service delivery-related features of an MTP so that the userperceives an output of an inference engine in a manner most suitable foran individual user.

In an example, the multidimensional context may include at least one ofuser or life occurrence location information. In an example, themultidimensional context may include at least one of time of lifeoccurrence and current time. The multidimensional context derived fromuser transactions may be used by the MTP 102 to determine lifeoccurrences and for generating resolutions paths. In an example, the MTPexpert engine 104 may use the multidimensional context derived from theuser transactions handled through the MTP 102 to generate actiontrigger-events for resolving life occurrences by facilitating userdirected mobile actions.

As depicted, the MTP 102 may utilize automated algorithms, learning andknowledge systems, discovery systems, inference engines for enablingintelligent solutions through the expert engine 104 in determining lifeoccurrences and determining available resolution paths. Further, thesesystems may facilitate in developing a customer-centric or user-centricexperience by utilizing user transactions data and recognizing usercontext through such as rules based systems, fuzzy systems, neuralnetwork and the like. These intelligent systems may facilitate aninteractive and collaborative communication between the temporal windowand the spatial window enabled through the expert engine 104 of the MTP102.

The expert engine 104 may determine a type of life occurrence of anindividual among a set of possible life occurrences based on amultidimensional data set, and may generate a resolution path forresolving aspects of the occurrence via uses of a life occurrence node(e.g. the individual's mobile device). The resolution path may be basedon an overall context of the individual that includes the point in timeat which the determination is made, data from a mobile transactionplatform (MTP) through which the individual conducts mobiletransactions, data from a third party source relating to the individual,and location data for the individual at the point in time. The locationdata and the temporal data may be coordinated through the temporalwindow and the spatial window by the expert engine 104 for determinationof the life occurrence and determination of the available resolutionpaths.

With reference to FIG. 23 there is illustrated an interconnection of theexpert engine 104 with the MTP 102. The expert engine 108 communicates avariety of types data and performs a range of functions with the MTP102. Data types include data, notification, alerts, suggestions, time,location, and the like. Functions include trigger bus exchange,synchronization, reconciling temporal/spatial windows for contextualconsistency.

With reference to FIG. 23, there is illustrated the exchange ofinformation between the expert engine 108 and the MTP 102 according toan exemplary and non-limiting embodiment. As illustrated, various formsof data are exchanged between the expert engine 104 and the MTP 102including, but not limited to, trigger bus data, sync data,notifications, alerts, suggestions, temporal data, and the like.

In accordance with the description above, various exemplary andnon-limiting embodiments enable an intuitive and seamless userexperience wherein applications drive potential transactions. While thesystem 2300 is a general purpose architecture that may be adapted to anyscenario, domain, transaction category or the like, various verticalapplication spaces are enabled including, not limited to, finance,retail, health care and government services. The system 2300 furtherenables the incorporation and seamless integration of a plurality ofpayment channels including, but not limited to, NFC, bar/QR codes,cloud, online and offline payment channels. The expert engine 108utilizes proactive intelligence in the form of user inputs, host rules,behavioral analytics and the like. As described above, the expert engine104 incorporates various context drivers including, but not limited totime and location drivers to produce, for example, pushnotifications/alerts in the form of triggered occurrences.

In accordance with exemplary and non-limiting embodiments, the userexperience as realized via, for example, a graphical user interface(GUI) of the lifestyle container 408 might be customized. For exampledefault display of either an alert centric or a notification centricperspective may be customized, whether or not a panel in which an actionis presented to the user is opened or merely executed when the userselects the action, and the like.

In an example, the MTP 102 and the expert engine 104 may synchronouslycommunicate and exchange information such as data, triggers,time-location information, notifications, alerts, suggestions, temporalor spatial window-based information and the like. As already discussedabove in conjunction with various figures, the expert engine 102 mayimplement intelligent learning solutions through use of such as fuzzylogic, neural networks, inference engines or systems and the like. TheMTP 102 may also associate information related to users such as bymaintaining a user database 2302 that may incorporate user transactionrelated details also. The MTP 102 may be communicatively linked with anID management system 2304.

Referring to FIG. 24, an enhanced mobile transaction platform (MTP) 102is depicted that provides services and solutions for a variety ofenvironments using multiple client-side delivery models 2402 over allpayment and transaction channels and environments. A client app 2406provided on the phone seamlessly interfaces with a server application2408 to enable transactions across a range of service providers andpoint of sale (POS) instruments. The enhanced MTP 102 includes robustinfrastructure and interface 2404 to and through the mobile deviceresources while facilitating an aggregation of disparate domainsincluding retail, finance, health, government, business, and otherservice providers. The client app 1706 residing on the phone interactswith an MTP enabling layer 2410 to interface with the service providersand point of sale (POS) instruments. The enabling layer 2410 of the MTP102 may comprise wallet management applications, NFC channels, barcodesystems and applications, widget management applications, and securecommunication and transaction engine.

Referring to FIG. 25, the methods and systems of mobile lifestyle andlife occurrence handling may be based on a set of guiding principlesthat deliver minimal intrusions on the user while maximizing usabilityof a mobile-enabled ecosystem for secure personalized transactions. Theprinciples ensure that a user centric experience provides seamlessinterfacing of applications that drive transactions across verticals,payment channels, and input sources. The guiding principles also ensurethat the user's experience is balanced among key aspects such as tokens2502 (e.g. cards, receipts, coupons, etc.), alerts 2504 (for keeping theuser on track), and notifications 2506 that address what a user wants todo rather than what the user has to do. In addition, a mobile lifestyleand life occurrence handling environment based on such guidingprinciples may include a context that is driven by time and location;provides specific instructions and exceptions (e.g. through pushnotifications and alerts), and frames the experience in the form ofsuggestions and recommendations that are closely coupled to lifeoccurrences of or related to the user.

In accordance with the description above, various exemplary andnon-limiting embodiments enable an intuitive and seamless userexperience wherein applications drive potential transactions. While thesystem 100 is a general purpose architecture that may be adapted to anyscenario, domain, transaction category or the like, various verticalapplication spaces are enabled including, not limited to, finance,retail, health care and government services. The system 100 furtherenables the incorporation and seamless integration of a plurality ofpayment channels including, but not limited to, NFC, bar/QR codes,cloud, online and offline payment channels. The expert engine utilizesproactive intelligence in the form of user inputs, host rules,behavioral analytics and the like. As described above, the expert engine104 incorporates various context drivers including, but not limited totime and location drivers to produce, for example, pushnotifications/alerts in the form of triggered occurrences.

The user experience as realized via, for example, a graphical userinterface (GUI) of the lifestyle container might be customized. Forexample default display of either an alert centric or a notificationcentric perspective may be customized, whether or not a panel in whichan action is presented to the user is opened or merely executed when theuser selects the action, and the like. With reference to FIG. 26, thereis illustrated an exemplary and non-limiting embodiment of a tokencentric user interface 2600, an alert centric user interface, and anotification centric user interface. A user may shift between thesethree different modes to view context-generated information incustomizable fashion.

FIG. 27 depicts an embodiment of a lifestyle user interface (UI) 2700that is also referred to herein as an activity feed or screen forfacilitating interactions of a user with the life occurrence handlingmethods and systems described herein. The lifestyle user interface 2700may be presented on a display of a life occurrence node, such as amobile phone.

The user interface 2700 comprises a plurality of moving panels 2702 thatutilize the portion of the life occurrence node display that isallocated to the activity feed to provide timely, contextual updates andlife occurrence-related information to a user. An activity feed maypresent life occurrences, trigger-events, offers, resolution actions,alerts, and the like related to life occurrences as may be determined byan MTP expert engine as described herein. Any of the plurality ofmovable panels 2702 may dwell in a position for a while, move to anotherposition, move out of view of the user, and the like based on a range ofparameters associated with life occurrences, such as those parametersfound in a multi-dimensional database described herein. Panels withcontent that is considered to be more urgent or important may remainvisible in the activity feed for a longer time than other panels.Important or urgent panels may be moved toward the top of the userinterface to assist with emphasis for the user. Important or urgentpanels may appear more frequently or may reappear sooner in the activityfeed than panels with less important content. Panels may also beactionable by a user, such as by the user selecting the panel to revealadditional details, or other content relevant to the life occurrenceassociated with the panel.

In the illustrated example, the activity screen 2700 displays a movingpanel 2702 a related to a prepaid account/card for Chicago Conventionand Tourism. The moving panel 2702 a further shows the account balanceamount and Chicago Convention and Tourism bureau branding along withuser actionable options to reload. This panel may be presented to theuser based on user preferences and/or life occurrence-relatedmulti-dimensional context that impacts when such an account should bepresented for reload. In this case, the user may have opted to have thecard reload action be presented for user acceptance rather than the MTPautomatically executing transactions to effect a reload.

The activity screen 2700 further includes another moving panel 2702 bthat displays Chicago transit related information such as name of thetransit authority, balance amount, a transit-related alert, and thelike. In this example of the activity feed user interface 2700, the CTApanel is dynamically moving laterally off of the display. This may occurfor a wide range of reasons including, without limitation, that a userhas swiped away this panel; the panel may have been presented to theuser for longer than a presentation threshold; the alert noted in thepanel may have expired; and other such reasons.

The activity screen 2700 further includes healthcare moving panel 2702 crelated to a hospital or other healthcare service provider. Thehealthcare moving panel 2702 c displays information about a lifeoccurrence that includes an upcoming appointment of the user with Dr.Sing who is associated with Hospital of Saint Raphael. The healthcaremoving panel 2702 c may include interactive features that allow the userto address aspects of this life occurrence. Through this panel, the usermay retrieve more information about the appointment and associatedhospital facilities. The healthcare moving panel may include featuresfor accessing options such as appointment details, insuranceinformation, travel directions to the appointment, and the like. Theuser may touch or click one of these options to present respectiveinformation in the user interface. FIGS. 35 and 36 include examples ofthese options.

The moving panels 2702 can be moved relative to one another such asshown in FIG. 28. As depicted in FIG. 28, the healthcare moving panel2702 c has moved up at the top position unlike in the previous where thehealthcare moving panel 2702 c was placed at the bottom position.Likewise, a new moving panel 2702 d related to a prepaid MasterCard hasalso been presented. In addition, corresponding activity screen 2800 nowincludes a new third moving panel 2702 e related to Loblaws. Acomparison of the FIG. 27 user interface 2700 and the FIG. 28 userinterface 2800 depicts relative movement of the moving panels 2702 andappearance of new moving panels on a user interface and disappearing ofsome moving panels from the user interface. Panel movement and dwelltime may be based on contextual or multidimensional information or othertypes of information retrieved by the MTP-Expert Engine from a pluralityof data sources.

FIG. 29 depicts another user interface 2900 that shows a new movingshopping panel 2702 f positioned on top of the activity screen or the2900 causing other moving panels including the prepaid card moving panel2702 d and the healthcare moving panel 2702 c to move down the activityscreen 2900. The Loblaws moving panel 2702 e no more exists on the userinterface 2900 and is moved out. The 2900 may further show alerts 2902related to various moving panels 2702. Alerts, that may be describedelsewhere herein might be associated with a trigger action event of alife occurrence. The platform may configure one or more mobiletransactions for executing with the MTP in response to a user takingsome action in response to the alerts. The alerts may for example resultas a consequence of the MTP-Expert Engine generating availableresolution paths and configuring presentations to the user through suchalerts for implementing a plurality resolution paths associated with aplurality of life occurrences. For example, as depicted, the shoppingmoving panel 2702 f includes three such alerts that may be of interestto the user. If the user finds these alerts as non-relevant, he may justdecline and skip them. Of course, the user is not required to take anyaction based on presentation of an alert.

FIG. 30 depicts another user interface 3000 that shows how screen spacemay be utilized by manipulating locations and sizes of the variousmoving panels 2702 on the user interface 3000. A new moving panel 2702 grelated to travel appears on the user interface 3000. Further, anothermoving panel 2702 h that relates to a birthday reminder appears on the3000 upon the MTP-Expert Engine identifying about an approachingbirthday of Mehul from the contextual and multidimensional informationassociated with the user through previous mobile transactions and otherdata sources. As the space on the 3000 is limited and no more space isavailable for more moving panels, therefore, considering the importanceand urgency of the moving panel 2702 h, it is presented as a banner overthe moving panel 2702 g overlaying a portion of the moving panel 2702 g.In other embodiments, not depicted, however, the user may have an opento resize dimensions of the various moving panels 2702 so as toaccommodate more or fewer simultaneously presented moving panels.

FIG. 31 depicts another exemplary user interface 3100 that comprises afew more examples of moving panels such as including a flight movingpanel 2702 i positioned on top of the interface 3100. The flight movingpanel 2702 i alerts the user about a delay in the scheduled flight withoptions for more details. As discussed in conjunction with variousembodiments in this document, the MTP-Expert Engine may be configured todetermine an impact on potential life occurrences related to the flightdelay. The information used to present such an urgent panel may bederived from user past transactions for air travel, a user calendar ofevents that are close in time to the originally scheduled arrival time,flight information, and the like. The MTP-Expert Engine may furtherdetermine a plurality of available resolution paths associated with thelife occurrence of flight delay and present them to the user as depictedin, for example, FIG. 40. For example, the options provided to the userin the flight moving panel 2702 i may link the user to informationcontaining various available resolution paths. This may for exampleinclude without limitations, alternative flight schedules, arrangementin next earliest flights, hotel stay nearby and the like withoutlimitations. The MTP-Expert Engine may use a plurality of intelligentsolutions, capabilities, or algorithms for generating the resolutionpaths and presenting them on a new panel that is described inassociation with FIG. 40 the flight moving panel 2702 i. These may forexample include without limitations, fuzzy logic, neural networks, anddefined rules etc. and have been discussed in this document elsewhere.

Also, the MTP-Expert Engine may also generate resolution paths forMehul's birthday and may accordingly present gift solutions when theuser selects the moving panel banner 102 h. The resolution paths aboutsuggesting a gift that Mehul may like may be determined based on socialnetwork profile information of Mehul, available gift solutions for hisage group, nearby gift shops' inventory, and the like. The user mayclick on the moving panel 2702 h or activate the associated options inother manners to execute one or more of various available pathsassociated with the birthday alert. FIGS. 33 and 34 show exemplarydetail panels that may be presented in response to a user selectingpanel 2702 h.

FIG. 32 illustrates an enlarged interface portion or moving panel 2702 hdepicting the banner for Mehul's birthday which may be presented to theuser on a portion of the user interface 3100 or on any other screenbased on user preferences. The banner may provide an option to searchfor more details about what Mehul may like as a birthday gift. Forexample, FIGS. 33 and 34 depict interfaces 3300 and 3400 that show moredetails after the user selects the option of viewing more details fromthe banner or the moving panel 102 h.

As shown in FIG. 33, the MTP-Expert Engine may suggest a birthday gift.For example, the MTP-Expert Engine may utilize contextual informationand the multidimensional information to recognize that Mehul hadpurchased a shirt in the recent past and therefore a matching cuff linkmay be a good option for him as a birthday gift. Therefore, amongvarious other options, the MTP-Expert Engine suggests Mont BlancPlatinum Cuff-Link. The 3300 may also present purchase options alongwith options for shipping. The MTP-Expert Engine may also identifypossible saving schemes (e.g. offers) and report them to the user andupdate him about total savings through the purchase. FIG. 34 depicts theuser interface 3400 showing checkout options to purchase the birthdaygift. The user can buy the gift and select a checkout option by usingany of his registered credit cards for which options may be displayed tothe user and presented.

FIG. 35 depicts an exemplary user interface 35 of details for the user'sDr. Sing appointment shown in healthcare moving panel 2702 c. The userinterface 35 may be presented to the user when the user selects movingpanel 2702 c. When the user accesses the option for more details, theuser is presented his medical checkup details for example cardiologyrecords of the user in this case. The presented details are determinedby the MTP-Expert Engine based on information derived from themultidimensional context, user preferences, user past transactions, andthe like. The MTP-Expert Engine may also determine information about theparticular appointment with Dr. Sing by accessing a healthcare portal ofthe user associated with the doctor, the hospital, or both. For example,in the exemplary case depicted in FIG. 35, the MTP-Expert Enginedetermines that the user needs a prostate checkup and therefore,provides another option for the user to learn more about the prostatecheckup procedure as depicted in FIG. 36 that is described below.

In an aspect of the present invention, the MTP-Expert Engine may alsoshow actions that the system has already taken care of based on userpreferences, managed on-device settings and the like for automatic lifeoccurrence resolution actions. For example, the user interface 3500 maydisplay that a financial obligation related to the appointment (e.g. aco-pay) will be take care of automatically with the user's prepaidMasterCard. In addition, the MTP Expert engine has automaticallyarranged for insurance information to be updated (e.g. the user'sinsurance card details have been transmitted to the insurance carrier).

FIG. 3600 depicts a detailed user interface 3600 for the particularprocedure that the user is scheduled for with Dr. Sing in the ‘Hospitalof Saint Raphael’. This content may be displayed in response to the userselecting the “Read about prostate checkup” option in 3500. The contentdisplayed in 3600 may be derived from various sources including Internetsources. The MTP-expert engine may determine the best sources for suchinformation based on user and other reviews of content presented onvarious websites, prior user access to medical information, and/or userpreferences for such information. In this way, the user may accordinglyprepare for the procedure before actually visiting the doctor withouthaving to spend time researching various websites to determine whichwebsite content to read.

FIG. 37 depicts another example of a detailed user interface 3700 thatis presented when the user selects the prepaid card moving panel 2702 d.The 3700 shows various activities associated with the user MasterCard,such as bill payments and the like. In the illustrated example, the useris shown that current bill is exceptionally high. The MTP-Expert Enginemay compare the current bill with those of the historical bills andaccordingly interact with the user through the interface 3700 so as toalert him about the high bill and seek his approval for bill paymentprior to automatically paying the bill using the MasterCard of the useras is generally done for normal bill payments. The user interface 3700may also show options to view the bill in detail and also to confirm forpayment of the bill through the MasterCard associated with the movingprepaid card panel 2702 d. The user may also be provided with an optionto just ignore the bill and do nothing. The user interface 3700 may alsoshow the various recent payments that were automatically taken care ofby the lifestyle system or the MTP-Expert Engine based on userpreferences for automatically taking action. The user is always incontrol of how bills are paid, including thresholds that require manualauthorization, and the like.

FIG. 38 further depicts another example of a detailed user interface3800 that is presented when the user selects the shopping moving panel2702 f. The detail shopping 3800 shows shopping highlights includingactions that the lifestyle system has taken care of. For example, thelifestyle system determines a plurality of offers that may be related touser life occurrences and accordingly presents these offers to the userthrough 3800. The user may also scroll the user interface 3800 to viewdetails and actions that the user can take regarding various shoppingitems that the lifestyle system has performed automatically. Forexample, such a scrolled user interface 3800 is depicted in 39. The usercan view shopping lists or options to make payments for ordered itemsthrough this extended scrolled portion of the 39. FIG. 39 provides anexample of vertical scrolling of the user interface 39 to accommodatepresenting more details to the user.

FIG. 40 depicts another example of a detailed user interface 4000 thatis presented when the user selects the flight moving panel 2702 h. Uponselection of the flight moving panel that shows an air travel alertpertaining to a delay in the flight (see FIG. 29), details regardingflight delays are presented to the user through the detailed screen4000. The 4000 shows details related to the flight delays and other lifeoccurrences such as meetings and the like that may be impacted by thedelayed flight. The lifestyle system may automatically take certainactions based on user preferences such as proposing rescheduling ofmeetings in accordance with revise flight timings, rescheduling pickupservices and the like. Accordingly, various items that can beautomatically handled may be presented on user interface 4000. In somecases, the user approval of some aspect of the life occurrence may beneeded for the expert engine to provide available resolutions for thelife occurrences. For example, as shown in FIG. 40, the user interfacepresents options for updating the meetings and rescheduling them for adifferent time and possibly at a time different location. The user canact on any of these options by clicking the respective options presentedin this user interface.

Use Cases

The transactions triggers by a life occurrence management platform maybe in the form of time-based trigger-events that may be explicit orimplicit for example based on user-defined preferences (explicit) orfrom information derived from ecosystem or from MTP, ES, FB, IM, Skype(implicit) and the like. The trigger-events may in other cases be of thelocation type such as for transit environments or spatial fences. Forexample, in case of a transit environment, when a user goes to astation, a trigger-event that says the station that the user normallyuses is out of service. A life occurrence management platform may pointthe user to an alternative mode of transportation (which may be othermode of transport such as station for a bus), to parking for the othermode, to timetables, and the like based on available resolutionsassessed by the expert engine of a life occurrence management platform.

In accordance with an exemplary and non-limiting embodiment, the expertengine mines personal data of a mobile device user and compares it tothird party data (e.g. airline flight schedule data) and discovers thatthe user's flight out of Chicago has been delayed until the next day.The expert engine determines that the last time a similar delay occurredat LaGuardia Airport, the user stayed at a particular hotel at theairport. The expert engine sends an alert message to the mobile deviceof the user indicating the flight delay as well as suggested resolutionactions (e.g. making hotel reservations at the particular hotel) thatmay be confirmed in response to the occurrence. The alert message isdisplayed on the mobile device via the lifestyle container 106, therebyshowing the nature of the occurrence and the suggested resolutionincluding an option for confirming a hotel reservation at the airportand a rental car. Such a display is illustrated at FIG. 4.

By way of example, the expert engine mines personal data of a mobiledevice user and discovers that the user's brother has a birthday in 5days. The expert engine determines that the occurrence of the birthdayrequires a resolution action comprising, at least, purchasing a gift forthe brother. The expert engine notices that the user purchased and senta dress shirt last year in response to that birthday occurrence. Theexpert engine determines that a complimentary gift for this year is cufflinks and locates a pair of platinum Mont Blanc cuff links for sale towhich may be applied a 10% discount when purchased via a mobiletransaction using a Loblaws gift card. The expert engine sends an alertmessage to the mobile device enabling the lifestyle container indicatingthe impending birthday occurrence as well as suggested resolutionactions that may be confirmed in response to the occurrence. The alertmessage is displayed on the mobile device showing the nature of theoccurrence, the suggested resolution, a suggested method of shipment anda suggested use of the Loblaw card as illustrated in FIG. 4A. Withreference to FIG. 4A there is illustrated an exemplary and non-limitingembodiment of a user interface of a lifestyle container operating on amobile device, such as a mobile phone. Scrolling to the bottom of thescreen, as illustrated in FIG. 4B, there is displayed a suggested formof credit for the mobile transaction. By scrolling horizontally, theuser may choose a preferred form of payment for the mobile transactionand complete the resolution actions associated with the occurrence.

In accordance with another exemplary and non-limiting embodiment, a userfollows a similar route most weekday mornings from the subway station tohis office four blocks away. Most mornings he purchases a Stardollarcoffee from the first of four such Stardollar he passes. As theStardollar are individually franchised, different Stardollar may offerdifferent deals. Using a GPS location signal and a time stamp as spatialand temporal context drivers, the expert engine consolidates thelocation and time user data with transactional analytic data acquired bythe MTP relating to past purchases by the user to createmultidimensional context data indicative of the options available to theuser. For example, the expert engine may generate and transmit to theMTP a trigger with attendant context-based generated actions that aretriggered on the condition that the user has exited the subway andappears to be heading on the usual route to the office. Once the MTPand/or the expert engine and/or the container observe the triggercondition to be met, the user may be informed of the suggested actions.

For example, the user may receive an alert via the lifestyle containerthat he can purchase a coffee at a Stardollar one block away and on hisprospective route at a 10% discount using his Stardollar card. Upon theuser accepting the offer, the MTP seamlessly places the order for thecoffee in the name of the user and pays for it using the user'sStardollar credit card. As the user passes or prepares to pass theStardollar at which his coffee is waiting, his location is used totrigger an alert to remind him that his coffee is waiting.

Note that in this example, both location and time serve as contextdrivers for the generation of the triggers. If, for example, the userwas at this same place on a Saturday, this temporal context driver mightcause the expert engine to discount the probability that the user wouldbe following his normal weekday route. If, for example, the user was atthe same subway stop in the afternoon, the expert engine may use thetemporal context driver to determine that the user is heading to lunch.This context driven conclusion may be reinforced by access to a user'sFacebook posting that he is looking forward to having lunch with hisfriend on Saturday. As a result, the expert engine may generate one ormore triggers enabling making reservations or calling a taxi for theuser. In the above example, the location context driver of a GPSlocation may likewise drive the context driven trigger creation. Forexample, if the user exits a different subway stop than usual in themorning, the expert engine may conclude that the user is likely to stillwant coffee from a Stardollar, may search for a Stardollar near the userand may generate a trigger to alert the user.

In one exemplary and non-limiting embodiment, the expert enginedetermines from the multidimensional context that a user prefersJavaJeff coffee over Stardollar. In such an instance, the expert enginegenerates a trigger to alert the user to the possibility of obtainingcoffee at JavaJeff that may, for example, be a short distance from aStardollar in front of which the user is currently standing. The use oflocation based context drivers can aid in creating the multidimensionalcontext. In the present example, the expert engine may gather that aStardollar is directly next to or in very close proximity to JavaJeffstore when a user chooses to purchase a coffee at JavaJeff. Such anaction is a strong indicator that the user prefers JavaJeff coffee overStardollar coffee. The strength of the indicator varies in inverseproportion to the proximity of the two stores at the time of the user'schoice to purchase one brand over another.

Significant changes in purchasing patterns can serve as context drivers.In an example, the expert engine may observe from transaction analyticsthat a user has begun to purchase certain products, such as diapers, orin certain stores, such as Home Depot. Such changes may be indicative ofthe user becoming a parent or buying a house, respectively.

The expert engine may generate triggers related to a level of loyaltypoints. By analyzing transactions, the MTP can ascertain if a user'slevel of loyalty points is high or low. Such information is more thanjust knowledge of the mere membership of a user in a loyalty program.Once such information is consolidated into a multidimensional context,the expert engine may generate triggers to propose offers which areespecially attractive when the user redeems some of his loyalty points,or where an extra amount of loyalty points can be collected.

The status of a credit card or account may comprise a context driver.For example, if the expert engine determines how ‘strained’ a certaincredit card already is, then, depending on the amount to be paid, itmight propose using another card for a transaction. Also, the user mighthave a preference to pay for expensive goods (or travel-related things)with a specific credit card, because it offers some additional insurancethat are beneficial in that situation. The expert engine mayautomatically select this specific credit card based on the situation tohelp the user gain the most or most important benefit available.

In addition to time/date being part of a life occurrence descriptor ormetadata, one might add an ‘urgency+importance’ attribute to the lifeoccurrence descriptor. This ‘urgency+importance’ attribute is likely tobe very personal for each user (and its weight for determining its valueto the expert engine might change over time). The expert engine canlearn these variations in urgency and importance to the user and makeappropriate proposals. For example, a certain person just likes to payall bills and taxes absolutely on time, so the closer the due date ofthis kind of transaction comes the more prominent a certain element ofthe screen would become, such as being promoted to the top of anoccurrence list, increasing in size or changing in color, or having anagging UI dialogue. Another person is not so focused on the bills, butmore on the relationships, so for her a friend's birthday will be moreimportant as a reminder, because she needs to find the perfect present.Therefore, her friend's birthday occurrence may become more prominentuntil the event is resolved (e.g. by sending her friend a gift,attending a birthday party, and the like).

The expert engine might determine from the multidimensional context thata user has an upcoming doctor's appointment. In response, an alert maybe displayed to a user on via a GUI controlled by the lifestylecontainer 106. A visual indicia may guide the user to a GPS applicationthat is preprogrammed to direct the user from his current location to aparking lot in proximity to the doctor office for the appointment and,if desired, to the doctor's office. If this is the first time the userhas been to this doctor's office, the user may select a visual indiciacorresponding to an option to have the user's medical records securelytransferred to the doctor's office. Upon exiting the doctor's office,the expert engine may aggregate 3rd party content from the doctor'soffice to create a trigger for acquiring prescribed medication. Theexpert engine may identify one or more pharmacies on the user's way homeand offer a selection of pharmacies from which the user may choose adesired destination. Once chosen, the MTP may execute the back-endtransactions required to place the order for the user's prescription tobe picked up at a predetermined time.

While methods and systems of life occurrence management have beendisclosed in connection with the preferred embodiments shown anddescribed in detail, various modifications and improvements thereon willbecome readily apparent to those skilled in the art. Accordingly, thespirit and scope of any claims presented herein is not to be limited bythe foregoing examples, but is to be understood in the broadest senseallowable by law.

While the methods and systems of life occurrence management have beendescribed in connection with certain preferred embodiments, otherembodiments may be understood by those of ordinary skill in the art andare encompassed herein.

The methods and systems described herein may be deployed in part or inwhole through a machine that executes computer software, program codes,and/or instructions on a processor. The processor may be part of aserver, client, network infrastructure, mobile computing platform,stationary computing platform, or other computing platform. A processormay be any kind of computational or processing device capable ofexecuting program instructions, codes, binary instructions and the like.The processor may be or include a signal processor, digital processor,embedded processor, microprocessor or any variant such as a co-processor(math co-processor, graphic co-processor, communication co-processor andthe like) and the like that may directly or indirectly facilitateexecution of program code or program instructions stored thereon. Inaddition, the processor may enable execution of multiple programs,threads, and codes. The threads may be executed simultaneously toenhance the performance of the processor and to facilitate simultaneousoperations of the application. By way of implementation, methods,program codes, program instructions and the like described herein may beimplemented in one or more thread. The thread may spawn other threadsthat may have assigned priorities associated with them; the processormay execute these threads based on priority or any other order based oninstructions provided in the program code. The processor may includememory that stores methods, codes, instructions and programs asdescribed herein and elsewhere. The processor may access a storagemedium through an interface that may store methods, codes, andinstructions as described herein and elsewhere. The storage mediumassociated with the processor for storing methods, programs, codes,program instructions or other type of instructions capable of beingexecuted by the computing or processing device may include but may notbe limited to one or more of a CD-ROM, DVD, memory, hard disk, flashdrive, RAM, ROM, cache and the like.

A processor may include one or more cores that may enhance speed andperformance of a multiprocessor. In embodiments, the process may beexecuted on a dual core processor, quad core processors, otherchip-level multiprocessor and the like that combine two or moreindependent cores (called a die).

The methods and systems described herein may be deployed in part or inwhole through a machine that executes computer software on a server,client, firewall, gateway, hub, router, or other such computer and/ornetworking hardware. The software program may be associated with aserver that may include a file server, print server, domain server,Internet server, intranet server and other variants such as secondaryserver, host server, distributed server and the like. The server mayinclude one or more of memories, processors, computer readable media,storage media, ports (physical and virtual), communication devices, andinterfaces capable of accessing other servers, clients, machines, anddevices through a wired or a wireless medium, and the like. The servermay execute the methods, programs or codes as described herein andelsewhere. In addition, other devices required for execution of methodsas described in this application may be considered as a part of theinfrastructure associated with the server.

The server may provide an interface to other devices including, withoutlimitation, clients, other servers, printers, database servers, printservers, file servers, communication servers, distributed servers andthe like. Additionally, this coupling and/or connection may facilitateremote execution of program across the network. The networking of someor all of these devices may facilitate parallel processing of a programor method at one or more location without deviating from the scope. Inaddition, any of the devices attached to the server through an interfacemay include at least one storage medium capable of storing methods,programs, code and/or instructions. A central repository may provideprogram instructions to be executed on different devices. In thisimplementation, the remote repository may act as a storage medium forprogram code, instructions, and programs.

The software program may be associated with a client that may include afile client, print client, domain client, Internet client, intranetclient and other variants such as secondary client, host client,distributed client and the like. The client may include one or more ofmemories, processors, computer readable media, storage media, ports(physical and virtual), communication devices, and interfaces capable ofaccessing other clients, servers, machines, and devices through a wiredor a wireless medium, and the like. The methods, programs or codes asdescribed herein and elsewhere may be executed by the client. Inaddition, other devices required for execution of methods as describedin this application may be considered as a part of the infrastructureassociated with the client.

The client may provide an interface to other devices including, withoutlimitation, servers, other clients, printers, database servers, printservers, file servers, communication servers, distributed servers andthe like. Additionally, this coupling and/or connection may facilitateremote execution of program across the network. The networking of someor all of these devices may facilitate parallel processing of a programor method at one or more location without deviating from the scope. Inaddition, any of the devices attached to the client through an interfacemay include at least one storage medium capable of storing methods,programs, applications, code and/or instructions. A central repositorymay provide program instructions to be executed on different devices. Inthis implementation, the remote repository may act as a storage mediumfor program code, instructions, and programs.

The methods and systems described herein may be deployed in part or inwhole through network infrastructures. The network infrastructure mayinclude elements such as computing devices, servers, routers, hubs,firewalls, clients, personal computers, communication devices, routingdevices and other active and passive devices, modules and/or componentsas known in the art. The computing and/or non-computing device(s)associated with the network infrastructure may include, apart from othercomponents, a storage medium such as flash memory, buffer, stack, RAM,ROM and the like. The processes, methods, program codes, instructionsdescribed herein and elsewhere may be executed by one or more of thenetwork infrastructural elements.

The methods, program codes, and instructions described herein andelsewhere may be implemented on a cellular network having multiplecells. The cellular network may either be a frequency division multipleaccess (FDMA) network or a code division multiple access (CDMA) network.The cellular network may include mobile devices, cell sites, basestations, repeaters, antennas, towers, and the like. The cell networkmay be a GSM, GPRS, 3G, EVDO, mesh, or other type network.

The methods, programs codes, and instructions described herein andelsewhere may be implemented on or through mobile devices. The mobiledevices may include navigation devices, cell phones, mobile phones,mobile personal digital assistants, laptops, palmtops, netbooks, pagers,electronic books readers, music players and the like. These devices mayinclude, apart from other components, a storage medium such as a flashmemory, buffer, RAM, ROM and one or more computing devices. Thecomputing devices associated with mobile devices may be enabled toexecute program codes, methods, and instructions stored thereon.Alternatively, the mobile devices may be configured to executeinstructions in collaboration with other devices. The mobile devices maycommunicate with base stations interfaced with servers and configured toexecute program codes. The mobile devices may communicate on apeer-to-peer network, mesh network, or other communications network. Theprogram code may be stored on the storage medium associated with theserver and executed by a computing device embedded within the server.The base station may include a computing device and a storage medium.The storage device may store program codes and instructions executed bythe computing devices associated with the base station.

The computer software, program codes, and/or instructions may be storedand/or accessed on machine readable media that may include: computercomponents, devices, and recording media that retain digital data usedfor computing for some interval of time; semiconductor storage known asrandom access memory (RAM); mass storage typically for more permanentstorage, such as optical discs, forms of magnetic storage like harddisks, tapes, drums, cards and other types; processor registers, cachememory, volatile memory, non-volatile memory; optical storage such asCD, DVD; removable media such as flash memory (e.g. USB sticks or keys),floppy disks, magnetic tape, paper tape, punch cards, standalone RAMdisks, Zip drives, removable mass storage, off-line, and the like; othercomputer memory such as dynamic memory, static memory, read/writestorage, mutable storage, read only, random access, sequential access,location addressable, file addressable, content addressable, networkattached storage, storage area network, bar codes, magnetic ink, and thelike.

The methods and systems described herein may transform physical and/oror intangible items from one state to another. The methods and systemsdescribed herein may also transform data representing physical and/orintangible items from one state to another.

The elements described and depicted herein, including in flow charts andblock diagrams throughout the figures, imply logical boundaries betweenthe elements. However, according to software or hardware engineeringpractices, the depicted elements and the functions thereof may beimplemented on machines through computer executable media having aprocessor capable of executing program instructions stored thereon as amonolithic software structure, as standalone software modules, or asmodules that employ external routines, code, services, and so forth, orany combination of these, and all such implementations may be within thescope of the present disclosure. Examples of such machines may include,but may not be limited to, personal digital assistants, laptops,personal computers, mobile phones, other handheld computing devices,medical equipment, wired or wireless communication devices, transducers,chips, calculators, satellites, tablet PCs, electronic books, gadgets,electronic devices, devices having artificial intelligence, computingdevices, networking equipment, servers, routers and the like.Furthermore, the elements depicted in the flow chart and block diagramsor any other logical component may be implemented on a machine capableof executing program instructions. Thus, while the foregoing drawingsand descriptions set forth functional aspects of the disclosed systems,no particular arrangement of software for implementing these functionalaspects should be inferred from these descriptions unless explicitlystated or otherwise clear from the context. Similarly, it may beappreciated that the various steps identified and described above may bevaried, and that the order of steps may be adapted to particularapplications of the techniques disclosed herein. All such variations andmodifications are intended to fall within the scope of this disclosure.As such, the depiction and/or description of an order for various stepsshould not be understood to require a particular order of execution forthose steps, unless required by a particular application, or explicitlystated or otherwise clear from the context.

The methods and/or processes described above, and steps thereof, may berealized in hardware, software or any combination of hardware andsoftware suitable for a particular application. The hardware may includea general-purpose computer and/or dedicated computing device or specificcomputing device or particular aspect or component of a specificcomputing device. The processes may be realized in one or moremicroprocessors, microcontrollers, embedded microcontrollers,programmable digital signal processors or other programmable device,along with internal and/or external memory. The processes may also, orinstead, be embodied in an application specific integrated circuit, aprogrammable gate array, programmable array logic, or any other deviceor combination of devices that may be configured to process electronicsignals. It may further be appreciated that one or more of the processesmay be realized as a computer executable code capable of being executedon a machine-readable medium.

The computer executable code may be created using a structuredprogramming language such as C, an object oriented programming languagesuch as C++, or any other high-level or low-level programming language(including assembly languages, hardware description languages, anddatabase programming languages and technologies) that may be stored,compiled or interpreted to run on one of the above devices, as well asheterogeneous combinations of processors, processor architectures, orcombinations of different hardware and software, or any other machinecapable of executing program instructions.

Thus, in one aspect, each method described above and combinationsthereof may be embodied in computer executable code that, when executingon one or more computing devices, performs the steps thereof. In anotheraspect, the methods may be embodied in systems that perform the stepsthereof, and may be distributed across devices in a number of ways, orall of the functionality may be integrated into a dedicated, standalonedevice or other hardware. In another aspect, the means for performingthe steps associated with the processes described above may include anyof the hardware and/or software described above. All such permutationsand combinations are intended to fall within the scope of the presentdisclosure.

While the methods and systems described herein have been disclosed inconnection with certain preferred embodiments shown and described indetail, various modifications and improvements thereon may becomereadily apparent to those skilled in the art. Accordingly, the spiritand scope of the methods and systems described herein is not to belimited by the foregoing examples, but is to be understood in thebroadest sense allowable by law.

What is claimed is:
 1. An system, comprising: a first processor forexecuting code stored on a non-transient computer readable medium toconsolidate transactional analytics from a mobile transaction platform(MTP) with data from at least one third party source to produce a dataset comprising data representing aspects of a plurality of user-specificlife occurrences; a second processor adapted to determine, in real-timeor near real-time, a type of life occurrence of a person with whom aportion of the transactional analytics and a portion of the data fromthe at least one third party source is associated, the type of lifeoccurrence determined from among a set of possible life occurrences, theset of possible life occurrences based at least in part on the data set;a resolution path generation facility that generates a plurality ofresolution paths having a series of action events a portion of whichrequire responses from a user and leading to resolution of at least onelife occurrence of the determined type of life occurrence; and a digitalelectronic communications interface between the MTP and the resolutionpath generation facility that facilitates the sharing of responses tothe action events between the MTP and the resolution path generationfacility, wherein at least one of determining the type of lifeoccurrence and generating the plurality of resolution paths is based onthe shared responses.
 2. The system of claim 1, wherein thedetermination a type of life occurrence utilizes temporal data, spatialdata and risk assessment.
 3. The system of claim 1, wherein at least oneresponse from a user to one of the action events is via use of a lifeoccurrence node.
 4. The system of claim 3, wherein the life occurrencenode is a mobile device.
 5. The system of claim 1, wherein the processorfurther generates a multidimensional context data set used by the secondprocessor in determining a life occurrence type.
 6. The system of claim5, wherein the multidimensional context data set comprises vendorpersonalization of a vendor-specific application executing in aprocessor of a life occurrence node, context items comprising time,location, and importance of the determined life occurrence type and atleast one context item selected from a list of context items consistingof: a transaction detail, an urgency, the status of a credit card oraccount, mobile device use history, payment source, wallet state, typeof transaction, product/service, vendor, delivery method, deliveryarrangements, tax status, transaction participant, user preferences, thepresence of a network or a particular account, user associations with anon-vendor third-party, presence of vouchers and promotions, loyaltypoints, third-party user-related data, social network information, andcalendar information.
 7. The system of claim 1, wherein the secondprocessor is further adapted to determine an accuracy of thedetermination of a type of life occurrence and an appropriateness of atleast one of the series of action events based on the shared responses.8. The system of claim 7, wherein the set of possible life occurrencesis adapted based on at least one of the accuracy of determination of atype of life occurrence and an appropriateness of at least one of theseries of action events.
 9. The system of claim 7, wherein the secondprocessor provides the resolution path generation facility and theresolution path generation facility determines an appropriateness of atleast one of the series of action events based on the shared responses.10. The system of claim 1, wherein the MTP comprises the secondprocessor.